CN111307727B - Water body water color abnormity identification method and device based on time sequence remote sensing image - Google Patents
Water body water color abnormity identification method and device based on time sequence remote sensing image Download PDFInfo
- Publication number
- CN111307727B CN111307727B CN202010173305.8A CN202010173305A CN111307727B CN 111307727 B CN111307727 B CN 111307727B CN 202010173305 A CN202010173305 A CN 202010173305A CN 111307727 B CN111307727 B CN 111307727B
- Authority
- CN
- China
- Prior art keywords
- remote sensing
- wave band
- image
- sensing image
- water
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
- G01N21/274—Calibration, base line adjustment, drift correction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
- G01N2021/1797—Remote sensing in landscape, e.g. crops
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Immunology (AREA)
- Biochemistry (AREA)
- Health & Medical Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Analytical Chemistry (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a method and a device for identifying water color abnormity based on a time sequence remote sensing image, and belongs to the field of water environment pollution monitoring. Preprocessing the multi-time sequence remote sensing image to obtain water body remote sensing reflectivity images with different time sequences; carrying out spectrum statistical analysis on the remote sensing reflectivity image to obtain water body pixel statistical characteristic values of long-time sequences of different wave bands, and establishing reference characteristic values and pixel characteristic values of different wave bands. Carrying out spectral characteristic analysis on the remote sensing image to be detected, and establishing wave band characteristic values of different wave bands; preliminarily judging whether the water color abnormality exists in the image to be detected by comparing the waveband characteristic value of the image to be detected with the corresponding waveband reference characteristic value; and analyzing the bands with the water color abnormity of the image to be detected pixel by pixel, and comparing the band with the pixel characteristic value of the corresponding band to obtain the spatial distribution pixels with the water color abnormity to obtain the spatial distribution of the water color abnormity of the water body. The invention improves the accuracy and speed of water color abnormity identification.
Description
Technical Field
The invention relates to the field of water environment pollution monitoring, in particular to a method and a device for identifying water color abnormity of a water body based on a time sequence remote sensing image.
Background
With the development of industrialization and the aggravation of urbanization, unreasonable industrial layout and the sharp increase of transportation of hazardous chemicals, the safety and pollution risks of water environment are increased, water eutrophication and various water environment pollution events are frequent due to over-standard discharge, hazardous chemical leakage, excessive use of pesticides and chemical fertilizers and the like, even if industrial sewage is directly discharged to rivers, inshore waters, ponds and the like, the self-purification capacity of the water is seriously exceeded, and the water quality type water shortage of residents or even serious environment pollution events are caused. The water eutrophication and the water environment pollution can cause the water color to change, which leads to the abnormal water color of the water. In order to quickly and emergently respond to the sudden water environment event, the water color abnormity of the sudden water body needs to be quickly and accurately positioned, the emergency monitoring capability of an environmental department on the sudden water environment event is improved, and timely support is provided for an environmental management department.
At present, water color abnormal information of water caused by water eutrophication and various water environment emergencies is acquired mainly by means of combining ground survey and remote sensing technology. The traditional ground survey mainly comprises the steps that monitoring personnel carry out on-site monitoring on a water area by utilizing ships and various water quality monitoring instruments, the standard reaching state of water quality indexes is judged, and the distribution state of water color abnormity is comprehensively judged by combining visual means such as the difference of the color of on-site water and the color of surrounding water; in recent years, remote sensing technology plays an important role in investigating water color abnormity. Generally, remote sensing images are obtained by means of satellite remote sensing, aerial remote sensing or ground remote sensing and the like, after various preprocessing, images are subjected to characteristic index calculation, then a threshold value is set to judge all pixels on the images, and then spatial distribution of water color anomaly of a water body is obtained.
The traditional ground investigation method depends on manpower, needs to consume a large amount of manpower, material resources and financial resources to obtain the water color abnormal distribution condition of the ground water body, is time-consuming, labor-consuming and needs a large amount of capital investment, and is influenced by weather, road accessibility and the like to ensure that the investigation is not comprehensive. The remote sensing technology can solve some disadvantages of the traditional investigation and plays an important role. The interpretation and identification of the water color anomaly by the current remote sensing technology mainly focus on two aspects: firstly, establishing an interpretation index (namely a judgment threshold) of the water color anomaly based on prior knowledge, and secondly, detecting all pixels of the image one by one. Although the identification and extraction of the water color anomaly based on remote sensing have a certain effect, the determination of the judgment threshold in the identification and extraction of the water color anomaly by the current remote sensing technology does not have an effective, accurate and reliable method, and the judgment threshold determined according to the priori knowledge in the prior art is not necessarily well applicable, and may have deviation, so that the water color anomaly identification result is inaccurate and even wrong.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method and a device for identifying water color abnormity based on time sequence remote sensing images, and the invention improves the accuracy and speed of water color abnormity identification.
The technical scheme provided by the invention is as follows:
in a first aspect, the invention provides a method for identifying water color anomaly based on a time sequence remote sensing image, which comprises the following steps:
s100: acquiring a remote sensing image to be detected;
s200: preprocessing the remote sensing image to be detected to obtain a remote sensing reflectivity image of a water area part corresponding to the remote sensing image to be detected;
s300: calculating a wave band characteristic value of each wave band of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected, and comparing the wave band characteristic value of each wave band with a reference characteristic value of the corresponding wave band in a spectral database respectively to obtain the wave band with water color abnormality;
s400: comparing the pixel value of each position of each wave band with the water color anomaly to obtain a pixel with the water color anomaly;
s500: extracting the boundary of the pixel with the water color anomaly to obtain the distribution position and range of the water color anomaly;
s600: counting the number of pixels with abnormal water color, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels;
the method for determining the reference characteristic value and the pixel characteristic value comprises the following steps:
s100': acquiring multi-scene remote sensing image samples of different time sequences of a monitoring area, and storing the multi-scene remote sensing image samples into a spectral database;
s200': preprocessing the multi-scene remote sensing image sample to obtain a remote sensing reflectivity image of a normal water color part of the water body corresponding to the multi-scene remote sensing image sample;
s300': and carrying out statistical analysis on each wave band of the remote sensing reflectivity image of the normal water color part of the water body corresponding to the multi-scene remote sensing image sample to obtain a reference characteristic value of each wave band and a pixel characteristic value of each position of each wave band.
Further, the S300' includes:
s310': performing principal component analysis on a first wave band of a remote sensing reflectivity image of a normal water color part of a water body corresponding to the multi-scene remote sensing image sample to obtain a first principal component of the multi-scene remote sensing image sample in the first wave band;
s320': repeating S310' to obtain a first principal component of the multi-scene remote sensing image sample in each wave band;
s330': and determining a reference characteristic value of each band and a pixel characteristic value of each position of each band according to the first principal component of each band.
Further, the wave band characteristic value of each wave band is the average value of the remote-sensing reflectivity image of the water area part corresponding to the remote-sensing image to be detected in the wave band, the reference characteristic value of each wave band is the product of the average value of the remote-sensing reflectivity image of the water body water color normal part corresponding to all the remote-sensing image samples in the wave band and the characteristic value of the first main component of the wave band, and the pixel characteristic value of each position of each wave band is the product of the pixel average value of the remote-sensing reflectivity image of the water body water color normal part corresponding to all the remote-sensing image samples in the wave band in the position and the characteristic value of the first main component of the wave band.
Further, the method for determining the reference characteristic value and the pixel characteristic value further includes:
s400': adding new remote sensing image samples of a plurality of scene monitoring areas into the spectral database, and executing S200 'and S300' to obtain updated reference characteristic values of each wave band and pixel characteristic values of each position of each wave band.
Further, after S200, before S300, the method further includes:
s210: carrying out edge mask processing on the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected;
after S200 ', before S300', the method further includes: and carrying out edge mask processing on the remote sensing reflectivity image of the normal water color part of the water body corresponding to the multi-scene remote sensing image sample.
Further, the remote sensing image to be detected and the remote sensing image sample are multispectral remote sensing images or hyperspectral remote sensing images;
the pre-processing includes radiation correction, atmospheric correction, cloud removal processing, and water-land separation.
Further, after S400, before S500, the method further includes:
s410: marking the pixels with abnormal water color as 1, and marking the pixels without abnormal water color as 0 to obtain a binary image;
s500 further comprises: extracting the boundary of the part marked as 1 of the binary image to obtain the distribution position and range of the water color abnormality;
s600 further is: and counting the number of the pixels marked as 1 in the binary image, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels.
In a second aspect, the present invention provides a device for identifying water color anomaly based on time sequence remote sensing images, the device comprising:
the first acquisition module is used for acquiring a remote sensing image to be detected;
the first preprocessing module is used for preprocessing the remote sensing image to be detected to obtain a remote sensing reflectivity image of a water area part corresponding to the remote sensing image to be detected;
the wave band comparison module is used for calculating a wave band characteristic value of each wave band of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected, and comparing the wave band characteristic value of each wave band with a reference characteristic value of the corresponding wave band in the spectral database respectively to obtain the wave band with abnormal water color;
the pixel comparison module is used for comparing the pixel value of each position of each wave band with the water color abnormity with the pixel characteristic value of the corresponding position of the corresponding wave band in the spectrum database to obtain the pixel with the water color abnormity;
the boundary extraction module is used for extracting the boundary of the pixel with the water color anomaly to obtain the distribution position and range of the water color anomaly;
the area calculation module is used for counting the number of the pixels with abnormal water color and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels;
the reference characteristic value and the pixel characteristic value are determined by the following modules:
the second acquisition module is used for acquiring multi-scene remote sensing image samples of different time sequences in a monitoring area and storing the multi-scene remote sensing image samples into a spectral database;
the second preprocessing module is used for preprocessing the multi-scene remote sensing image sample to obtain a remote sensing reflectivity image of a normal water color part of the water body corresponding to the multi-scene remote sensing image sample;
and the statistical analysis module is used for performing statistical analysis on each wave band of the remote sensing reflectivity image of the water color normal part corresponding to the multi-scene remote sensing image sample to obtain a reference characteristic value of each wave band and a pixel characteristic value of each position of each wave band.
Further, the statistical analysis module comprises:
the main component analysis unit is used for carrying out main component analysis on a first waveband of a remote sensing reflectivity image of a water color normal part of the water body corresponding to the multi-scene remote sensing image sample to obtain a first main component of the multi-scene remote sensing image sample in the first waveband;
the circulating unit is used for repeating the principal component analysis unit to obtain a first principal component of the multi-scene remote sensing image sample in each wave band;
and a determining unit for determining a reference characteristic value of each band and a pixel characteristic value of each position of each band according to the first principal component of each band.
Further, the wave band characteristic value of each wave band is the average value of the remote-sensing reflectivity image of the water area part corresponding to the remote-sensing image to be detected in the wave band, the reference characteristic value of each wave band is the product of the average value of the remote-sensing reflectivity image of the water body water color normal part corresponding to all the remote-sensing image samples in the wave band and the characteristic value of the first main component of the wave band, and the pixel characteristic value of each position of each wave band is the product of the pixel average value of the remote-sensing reflectivity image of the water body water color normal part corresponding to all the remote-sensing image samples in the wave band in the position and the characteristic value of the first main component of the wave band.
Further, the apparatus further comprises:
and the updating module is used for adding new remote sensing image samples of a plurality of scene monitoring areas into the spectral database, and executing the second preprocessing module and the statistical analysis module to obtain updated reference characteristic values of each wave band and pixel characteristic values of each position of each wave band.
Further, the apparatus further comprises:
the first edge mask module is used for carrying out edge mask processing on the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected;
and the second edge mask module is used for carrying out edge mask processing on the remote sensing reflectivity image of the water body water color normal part corresponding to the multi-scene remote sensing image sample.
Further, the remote sensing image to be detected and the remote sensing image sample are multispectral remote sensing images or hyperspectral remote sensing images;
the pre-processing includes radiation correction, atmospheric correction, cloud removal processing, and water-land separation.
Further, the apparatus further comprises:
the binary image acquisition unit is used for marking the pixels with abnormal water color as 1 and marking the pixels without abnormal water color as 0 to obtain a binary image;
the boundary extraction module is further configured to: extracting the boundary of the part marked as 1 of the binary image to obtain the distribution position and range of the water color abnormality;
the area calculation module is further: and counting the number of the pixels marked as 1 in the binary image, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels.
The invention has the following beneficial effects:
1. the method is based on the multi-time-sequence remote sensing image, the spectral information base of the water body with normal water color is established, the statistical reference characteristic value is established band by band according to the acquired spectral information base of the water body with normal water color, and the threshold value is set, so that the objective and accurate threshold value can be acquired, and the objective actual condition is closer to.
2. Comparing the single-waveband statistical characteristic value of the remote sensing image to be detected with the reference characteristic value of the spectral information base, and rapidly judging whether the image to be detected has water color abnormality; and then, distinguishing all pixels with the water color abnormal wave band one by one to obtain the pixels with the water color abnormal. The invention only compares the wave bands with water color abnormity, thereby greatly reducing the workload of comparison, saving the time for judging the water color abnormity and improving the speed for identifying the water color abnormity.
Drawings
FIG. 1 is a flow chart of a water body water color anomaly identification method based on a time sequence remote sensing image according to the invention;
fig. 2 is a schematic diagram of a water body water color anomaly identification device based on a time sequence remote sensing image.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
Example 1:
the embodiment of the invention provides a water body water color anomaly identification method based on a time sequence remote sensing image.
The method comprises the following steps of establishing a spectrum database in advance, and determining a reference characteristic value and a pixel characteristic value through the spectrum database, wherein the steps comprise:
s100': and acquiring multi-scene remote sensing image samples of different time sequences of the monitoring area, and storing the multi-scene remote sensing image samples into a spectral database.
The remote sensing image sample is a multispectral remote sensing image or a hyperspectral remote sensing image, the multispectral remote sensing image is a remote sensing image containing a plurality of wave bands, the number of the wave bands is generally several to dozens, and the number of the hyperspectral wave bands is generally hundreds. In general, a multispectral remote sensing image is acquired from an image acquisition device (e.g., an imaging spectrometer mounted on a satellite).
In the prior art, the identification and extraction of the water color anomaly by the remote sensing technology are realized based on a single-scene remote sensing image, and the water color anomaly extraction based on the single-scene image has the following defects:
the water color abnormal extraction based on the single-scene remote sensing image development is based on an assumption that the water color abnormal phenomenon belongs to a small probability event, namely, the number of pixels of the water color abnormal water body accounts for a small part of the number of pixels of the water color normal water body, so that the number of the water color abnormal pixels does not affect the statistical value of the normal water body pixels from the statistical rule, or the effect is very little, and therefore the statistical characteristic value of the pixel value of the normal water body of the single-crystal remote sensing image can be used as a threshold value to judge the water color abnormal pixels. However, under the influence of various factors, the assumption is not satisfied in some cases, for example, the water color is abnormal due to green algae tide such as enteromorpha, and when the green algae tide is exploded in a large scale, the pixels with abnormal water color may occupy most or even all of the total number of the pixels of the single-scene remote sensing image. At the moment, the judgment threshold value for determining the water color abnormity based on the single-scene remote sensing image has larger deviation, so that the water color abnormity identification result is inaccurate and even wrong.
The invention uses multi-scene remote sensing image samples with different time sequences to determine the reference characteristic value and the pixel characteristic value (threshold value), compared with the threshold value determined based on single-scene remote sensing images, because the threshold value of the invention is calculated based on statistical information, the larger the data volume is, the closer the data volume is to the objective actual situation, the objective and accurate threshold value can be obtained, the extraction precision of the water color abnormity is improved, and especially the extraction precision of the water color abnormity caused by large-area environmental pollution or ecological disasters.
S200': and preprocessing the multi-scene remote sensing image sample to obtain a remote sensing reflectivity image of the normal water color part of the water body corresponding to the multi-scene remote sensing image sample.
The preprocessing can comprise radiation correction, atmospheric correction, cloud removal processing, water-land separation and the like, and the remote sensing reflectivity image of the water area part is obtained after preprocessing.
Radiometric correction refers to a process of correcting systematic and random radiation distortions or distortions due to external factors, data acquisition and transmission systems, and eliminating or correcting image distortions caused by radiation errors.
Atmospheric correction (atmospheric correction) is used for eliminating errors caused by atmospheric scattering, absorption and reflection.
Cloud removal treatment: due to climate reasons, it is sometimes difficult to obtain a completely cloud-free remote sensing image, and most remote sensing images are affected by cloud, shadows and aerosols projected by the cloud on the ground surface, and so on, so that the interference needs to be removed. The present invention is not limited to the cloud removal processing method, and for example, the cloud removal processing may be performed by using a red band threshold method.
The land and water separation is to use the information of specific wave bands to carry out edge detection on the image, detect the outlines of the island and the continent, cut the image and obtain the remote sensing reflectivity image of the water area part. Or cutting the remote sensing reflectivity image by using the known water body boundary to obtain the remote sensing reflectivity image of the water body part.
Because the threshold is determined by the multi-scene remote sensing image sample, and the threshold is the statistical characteristic of normal and abnormal water color, the threshold is determined by the method of performing statistical analysis on the part with normal water color, only the part with normal water color is needed, and the part with abnormal water color is removed after water and land are separated.
S300': and carrying out statistical analysis on each wave band of the remote sensing reflectivity image of the normal water color part of the water body corresponding to the multi-scene remote sensing image sample to obtain a reference characteristic value of each wave band and a pixel characteristic value of each position of each wave band.
In the invention, the reference characteristic value of a wave band and the pixel characteristic value of each position of the wave band are obtained by carrying out statistical analysis on all pixels of the wave band of the remote sensing reflectivity image. The invention does not limit the statistical method, and does not limit the specific conditions of the reference characteristic value and the pixel characteristic value, and the reference characteristic value and the pixel characteristic value can be the same or different; and the image element characteristic values of all the positions of each wave band can be the same or different.
After a spectral database is established by the method and the reference characteristic value and the pixel characteristic value are determined, the remote sensing image can be subjected to water color anomaly identification through the reference characteristic value and the pixel characteristic value, and the method comprises the following steps:
s100: and acquiring the remote sensing image to be detected.
The remote sensing image to be detected is also a multispectral remote sensing image or a hyperspectral remote sensing image, and the details are not repeated here.
S200: and preprocessing the remote sensing image to be detected to obtain a remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected.
The method for preprocessing the remote sensing image to be detected is similar to the method for preprocessing the multi-scene remote sensing image sample, and radiation correction, atmospheric correction, cloud removal processing, water-land separation and the like are also carried out, but the water color abnormal part is not removed.
S300: and calculating the wave band characteristic value of each wave band of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected, and comparing the wave band characteristic value of each wave band with the reference characteristic value of the corresponding wave band in the spectral database respectively to obtain the wave band with the abnormal water color.
In the present invention, the method for calculating the band feature value is not limited, but the reference feature value and the pixel feature value calculated by the statistical method used in S300' should be calculated by a corresponding method in this step.
In the prior art, all pixels of each band are compared with a set threshold, and the number of bands with high influence is large, so that the calculation speed is low. The step is to preliminarily screen whether each waveband is abnormal in water color or not, preliminarily judge whether the waveband is abnormal or not by comparing the waveband characteristic value of the waveband with a reference characteristic value, and if the waveband is not abnormal, do not compare the pixels of the waveband with a threshold one by one, so that the detection speed is improved.
S400: and comparing the pixel value of each position of each wave band with the corresponding pixel characteristic value of the corresponding wave band in the spectrum database to obtain the pixel with the water color anomaly.
S500: and carrying out boundary extraction on the pixels with the water color anomaly to obtain the distribution position and range of the water color anomaly.
S600: and counting the number of the pixels with abnormal water color, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels.
If the spatial resolution is 8m and the number of marked pixels is 100, the area of the polluted water body is 8m by 100-6400 m2。
Firstly, preprocessing an acquired multi-time sequence remote sensing image to acquire water body remote sensing reflectivity images of different time sequences; carrying out spectrum statistical analysis on the water body remote sensing reflectivity images with different time sequences to obtain water body pixel statistical characteristic values of long-time sequences with different wave bands, and establishing reference characteristic values and pixel characteristic values with different wave bands. Then carrying out spectral feature analysis on the remote sensing image to be detected, and establishing wave band feature values of different wave bands; preliminarily judging whether the water color abnormality exists in the image to be detected by comparing the waveband characteristic value of the image to be detected with the corresponding waveband reference characteristic value; analyzing the bands with the water color abnormity of the image to be detected pixel by pixel, comparing the band with the characteristic value of the pixel of the corresponding band to obtain the spatial distribution pixels with the water color abnormity, and finally obtaining the spatial distribution of the water color abnormity of the water body.
The invention has the following beneficial effects:
1. the method is based on the multi-time-sequence remote sensing image, the spectral information base of the water body with normal water color is established, the statistical reference characteristic value is established band by band according to the acquired spectral information base of the water body with normal water color, and the threshold value is set, so that the objective and accurate threshold value can be acquired, and the objective actual condition is closer to.
2. Comparing the single-waveband statistical characteristic value of the remote sensing image to be detected with the reference characteristic value of the spectral information base, and rapidly judging whether the image to be detected has water color abnormality; and then, distinguishing all pixels with the water color abnormal wave band one by one to obtain the pixels with the water color abnormal. The invention only compares the wave bands with water color abnormity, thereby greatly reducing the workload of comparison, saving the time for judging the water color abnormity and improving the speed for identifying the water color abnormity.
The present invention is not limited to the statistical method of the reference characteristic value and the pixel characteristic value, and in one example, the statistical method is Principal Component Analysis (PCA), in which: s300' includes:
s310': and performing principal component analysis on a first waveband of the remote sensing reflectivity image of the normal water color part of the water body corresponding to the multi-scene remote sensing image sample to obtain a first principal component of the multi-scene remote sensing image sample in the first waveband.
S320': and repeating S310' to obtain a first principal component of the multi-scene remote sensing image sample in each wave band.
S330': and determining a reference characteristic value of each band and a pixel characteristic value of each position of each band according to the first principal component of each band.
More specifically, the characteristic value of each band is the average value of the remote-sensing reflectivity image of the water area part corresponding to the remote-sensing image to be detected in the band, the reference characteristic value of each band is the product of the average value of the remote-sensing reflectivity image of the water color normal part corresponding to all the remote-sensing image samples in the band and the characteristic value of the first main component of the band, and the characteristic value of each pixel in each position of each band is the product of the average value of the remote-sensing reflectivity image of the water color normal part corresponding to all the remote-sensing image samples in the band in the position and the characteristic value of the first main component of the band.
After a spectral database of the water body with normal water color in different wave bands is established, the database can be dynamically updated, wherein the updating method comprises the following steps:
s400': adding new remote sensing image samples of a plurality of scene monitoring areas into the spectral database, and executing S200 'and S300' to obtain updated reference characteristic values of each wave band and pixel characteristic values of each position of each wave band.
The invention dynamically updates the spectral database, and aims to obtain objective and accurate threshold values, because the threshold values are calculated based on statistical information, the larger the data volume is, the closer the data volume is to the objective actual situation.
In order to solve the problem that the edge is inaccurate after land and water separation, the following steps after S200 and before S300 also include:
s210: and carrying out edge mask processing on the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected.
After S200 ', before S300', further comprising: and carrying out edge mask processing on the remote sensing reflectivity image of the normal water color part of the water body corresponding to the multi-scene remote sensing image sample.
The edge mask can correct the edge of the water area part obtained by land and water separation.
In order to conveniently count the pixels with abnormal water color, the method further comprises the following steps after S400 and before S500:
s410: marking the pixels with abnormal water color as 1, and marking the pixels without abnormal water color as 0 to obtain a binary image;
correspondingly, S500 is further: extracting the boundary of the part marked as 1 of the binary image to obtain the distribution position and range of the water color abnormality;
s600 further is: and counting the number of the pixels marked as 1 in the binary image, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels.
A specific calculation example is given below to explain the distance in detail, and the example takes a PCA calculation method as an example:
s1, obtaining multi-scene multi-spectrum or high-spectrum remote sensing image samples of different time sequences of a monitoring area.
S2, preprocessing the remote sensing image sample, including radiation correction, atmospheric correction, cloud removal processing and land-water separation, to obtain remote sensing reflectivity images of water areas with multiple scenes and different time sequences; the water area part only comprises the water body with the normal water color part of the water body.
And S3, performing edge mask processing on the multi-scene remote sensing image of the water area part to obtain a modified remote sensing reflectivity image of the water area part, so as to eliminate deviation of a normal water body pixel statistical characteristic value caused by inaccurate land and water boundary.
S4, performing principal component analysis on wave bands corresponding to the multi-scene remote-reflection sensing images of the water area part to obtain a group of first principal components of the water body with normal water color part in different wave bands, namely reference characteristic values, and specifically operating as follows:
let i view remote sensing reflectivity images be a1, a2, … … and ai respectively, the first wave band of the first view remote sensing reflectivity image is designated as a11, the second wave band is designated as a12 and … …, and so on, and the jth wave band of the ith view remote sensing reflectivity image is designated as aij.
S41, performing principal component analysis on the first waveband of the multi-scene remote sensing reflectivity image to obtain a first principal component P11 of the multi-scene remote sensing reflectivity image in the first waveband.
And S42, repeating the step S41, and repeating the steps in the same way to obtain a first principal component Pj1 of the jth wave band of the multi-scene remote sensing image.
S43: and calculating the mean value Y1 of the first waveband of all the remote sensing reflectivity images a1, a2, … … and ai, and multiplying the Y1 by the characteristic value of the first principal component P11 of the first waveband to obtain the reference characteristic value T1 of the first waveband.
And S44, repeating the step S43, and so on to obtain the reference characteristic value Tj of the j-th waveband.
S45: calculating the average value Y11 of the pixels at the positions of the first row and the first column of all the remote sensing reflectivity images a1, a2, … … and ai, and multiplying the Y11 by the characteristic value of the first main component P11 of the first waveband to obtain the pixel characteristic value T111 at the position of the first row and the first column of the first waveband.
S46, repeating the step S45, and so on to obtain the pixel characteristic value T1mn of each position of the first wave band, wherein m and n represent the row index and the column index of the pixel.
S47, repeating the steps S45-S46, and so on to obtain the pixel characteristic value Tjmn of each position of the jth wave band.
And S48, establishing a spectral database of the water body with normal water color in different wave bands, and dynamically updating the database as required.
The specific process of dynamic update is as follows: adding a new one-scene or multi-scene remote sensing image of the monitoring area, and repeating the steps S1-S3;
and supplementing the spectral data of the first waveband of the new remote sensing image into the spectral database, and recalculating the first principal component to obtain the updated first principal component P11 of the first waveband.
S49, repeating the steps S41-S48 to obtain the updated first main component Pj1 of the j-th waveband, and calculating to obtain the reference characteristic value Tj and the pixel characteristic value Tjmn of the j-th waveband.
S5, obtaining the remote sensing image to be detected, and repeating the steps S1-S3 to obtain the remote sensing reflectivity image of the water body part of the remote sensing image to be detected.
S6, preliminarily screening whether the image to be detected is abnormal or not, and aiming at improving the water color abnormal detection speed: comparing the mean value Xj of the jth waveband image of the remote sensing reflectivity image of the water body part to be detected with the reference characteristic value Tj of the same waveband in the spectral database, and preliminarily judging whether the water color of the image to be detected is abnormal; the specific process is as follows:
s61, comparing the reference characteristic value Tj obtained in the step S49 of synchronizing the mean value X1 of the first wave band of the remote sensing image to be detected, wherein:
if the X1 is not similar to the T1, the image to be detected is abnormal, and the judgment is finished; if X1 is similar to T1, the detected image has no water color anomaly. And judging the next wave band.
If the X2 is not similar to the T2, the image to be detected is abnormal, and the judgment is finished; if X2 is similar to T2, the detected image has no water color anomaly. And judging the next wave band.
……
And S62, by analogy, comparing the mean value of all the wave bands of the image to be detected with the mean value of the first main components of all the wave bands in the spectral database to obtain the wave band j with the water color abnormal image.
S7, positioning and extracting the water color abnormity of the wave band which is preliminarily judged to have the water color abnormity: carrying out pixel-by-pixel discrimination on the remote sensing image wave band j with the water color abnormity after preliminary screening, and judging whether the remote sensing image wave band j belongs to the water color abnormity pixel; the specific process is as follows:
s71, comparing the wave band j (the wave band with the water color abnormality of the image to be detected) with the pixel characteristic value Tjmn of the corresponding wave band, and concretely, the following steps are carried out:
pixel Xj of the first row and the first column of the wave band j11Comparing the characteristic value with the pixel characteristic value Tj11 of the corresponding wave band (namely the pixel characteristic value of the first row and the first column of the j wave band) in the spectrum database;
if Xj11Similar to Tj11, the pel Xj11Labeled 0; if Xj11Is not similar to Tj11, the pixel Xj is11Labeled 1.
And S72, repeating the step S71, and comparing all the pixels with the water color abnormal wave band in sequence to obtain a new binary image.
And S8, carrying out boundary extraction on the binary image to obtain the distribution position and range of the water color anomaly.
And S9, carrying out pixel statistics on the binary image, acquiring the number of all pixels with the pixel value of 1, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels.
Therefore, by identifying the defects of the prior art, the invention provides the water color anomaly identification method based on the time sequence remote sensing image, which is used for accurately extracting the distribution position and area of the water color anomaly and obtaining the accurate distribution (position and area) of the water color anomaly caused by environmental pollution or ecological disasters, and is particularly suitable for the situation that the threshold value is difficult to determine due to the fact that the water color anomaly area of a single-scene image is large. The invention comprehensively sets a judgment threshold value for judging the water color abnormity by comprehensively analyzing the statistical characteristics of the multi-scene remote sensing image pixel based on the long-time sequence, and dynamically updates to realize the water color abnormity judgment of the remote sensing image pixel to be detected. The method can avoid the problem that the water color abnormity extraction error is large (or wrong) due to inaccurate setting of the large-area threshold of the polluted area of the monoscopic image, and improve the extraction precision of the water color abnormity, especially the extraction precision of the water color abnormity of the water body due to large-area environmental pollution or ecological disasters.
Example 2:
the embodiment of the invention provides a water color abnormity recognition device based on a time sequence remote sensing image, and as shown in figure 2, the device comprises:
the first acquisition module 1 is used for acquiring the remote sensing image to be detected.
The first preprocessing module 2 is used for preprocessing the remote sensing image to be detected to obtain a remote sensing reflectivity image of a water area part corresponding to the remote sensing image to be detected.
And the wave band comparison module 3 is used for calculating a wave band characteristic value of each wave band of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected, and comparing the wave band characteristic value of each wave band with a reference characteristic value of the corresponding wave band in the spectral database respectively to obtain the wave band with abnormal water color.
And the pixel comparison module 4 is used for comparing the pixel value of each position of each wave band with the abnormal water color with the pixel characteristic value of the corresponding position of the corresponding wave band in the spectrum database to obtain the pixel with the abnormal water color.
And the boundary extraction module 5 is used for performing boundary extraction on the pixels with the water color anomaly to obtain the distribution position and range of the water color anomaly.
And the area calculation module 6 is used for counting the number of the pixels with abnormal water color and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels.
The reference characteristic value and the pixel characteristic value are determined by the following modules:
the second acquisition module is used for acquiring multi-scene remote sensing image samples of different time sequences in a monitoring area and storing the multi-scene remote sensing image samples into the spectral database;
the second preprocessing module is used for preprocessing the multi-scene remote sensing image sample to obtain a remote sensing reflectivity image of a normal water color part of the water body corresponding to the multi-scene remote sensing image sample;
and the statistical analysis module is used for performing statistical analysis on each wave band of the remote sensing reflectivity image of the water color normal part corresponding to the multi-scene remote sensing image sample to obtain a reference characteristic value of each wave band and a pixel characteristic value of each position of each wave band.
Firstly, preprocessing an acquired multi-time sequence remote sensing image to acquire water body remote sensing reflectivity images of different time sequences; carrying out spectrum statistical analysis on the water body remote sensing reflectivity images with different time sequences to obtain water body pixel statistical characteristic values of long-time sequences with different wave bands, and establishing reference characteristic values and pixel characteristic values with different wave bands. Then carrying out spectral feature analysis on the remote sensing image to be detected, and establishing wave band feature values of different wave bands; preliminarily judging whether the water color abnormality exists in the image to be detected by comparing the waveband characteristic value of the image to be detected with the corresponding waveband reference characteristic value; analyzing the bands with the water color abnormity of the image to be detected pixel by pixel, comparing the band with the characteristic value of the pixel of the corresponding band to obtain the spatial distribution pixels with the water color abnormity, and finally obtaining the spatial distribution of the water color abnormity of the water body.
The invention has the following beneficial effects:
1. the method is based on the multi-time-sequence remote sensing image, the spectral information base of the water body with normal water color is established, the statistical reference characteristic value is established band by band according to the acquired spectral information base of the water body with normal water color, and the threshold value is set, so that the objective and accurate threshold value can be acquired, and the objective actual condition is closer to.
2. Comparing the single-waveband statistical characteristic value of the remote sensing image to be detected with the reference characteristic value of the spectral information base, and rapidly judging whether the image to be detected has water color abnormality; and then, distinguishing all pixels with the water color abnormal wave band one by one to obtain the pixels with the water color abnormal. The invention only compares the wave bands with water color abnormity, thereby greatly reducing the workload of comparison, saving the time for judging the water color abnormity and improving the speed for identifying the water color abnormity.
The statistical analysis module comprises:
and the main component analysis unit is used for carrying out main component analysis on a first waveband of the remote sensing reflectivity image of the water color normal part of the water body corresponding to the multi-scene remote sensing image sample to obtain a first main component of the multi-scene remote sensing image sample in the first waveband.
And the circulating unit is used for repeating the principal component analysis unit to obtain the first principal component of the multi-scene remote sensing image sample in each wave band.
And a determining unit for determining a reference characteristic value of each band and a pixel characteristic value of each position of each band according to the first principal component of each band.
The wave band characteristic value of each wave band is the average value of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected in the wave band, the reference characteristic value of each wave band is the product of the average value of the remote sensing reflectivity image of the water color normal part corresponding to all the remote sensing image samples in the wave band and the characteristic value of the first main component of the wave band, and the pixel characteristic value of each position of each wave band is the product of the pixel average value of the remote sensing reflectivity image of the water color normal part corresponding to all the remote sensing image samples in the wave band in the position and the characteristic value of the first main component of the wave band.
The apparatus of the present invention further comprises:
and the updating module is used for adding new remote sensing image samples of a plurality of scene monitoring areas into the spectral database, and executing the second preprocessing module and the statistical analysis module to obtain updated reference characteristic values of each wave band and pixel characteristic values of each position of each wave band.
And the first edge mask module is used for carrying out edge mask processing on the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected.
And the second edge mask module is used for carrying out edge mask processing on the remote sensing reflectivity image of the water body water color normal part corresponding to the multi-scene remote sensing image sample.
The remote sensing image to be detected and the remote sensing image sample are multispectral remote sensing images or hyperspectral remote sensing images.
The aforementioned pretreatments include radiation correction, atmospheric correction, dehazing, and land-water separation.
The apparatus of the present invention further comprises:
and the binary image acquisition unit is used for marking the pixel with the abnormal water color as 1 and marking the pixel without the abnormal water color as 0 to obtain a binary image.
The boundary extraction module is further: and (4) performing boundary extraction on the part marked as 1 by the binary image to obtain the distribution position and range of the water color anomaly.
The area calculation module further comprises: and counting the number of the pixels marked as 1 in the binary image, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels.
The device provided by the embodiment of the present invention has the same implementation principle and technical effect as the method embodiment, and for the sake of brief description, reference may be made to the corresponding content in the method embodiment 1 without reference to the device embodiment. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
It should be noted that, the above-mentioned apparatus or system in this specification may also include other implementation manners according to the description of the related method embodiment, and a specific implementation manner may refer to the description of the method embodiment, which is not described herein in detail. The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class, storage medium + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for the relevant points, refer to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures are not necessarily required to be in the particular order shown or in sequential order to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element.
As will be appreciated by one skilled in the art, one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the present invention in its spirit and scope. Are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (7)
1. A water body water color abnormity identification method based on a time sequence remote sensing image is characterized by comprising the following steps:
s100: acquiring a remote sensing image to be detected;
s200: preprocessing the remote sensing image to be detected to obtain a remote sensing reflectivity image of a water area part corresponding to the remote sensing image to be detected;
s300: calculating a wave band characteristic value of each wave band of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected, and comparing the wave band characteristic value of each wave band with a reference characteristic value of the corresponding wave band in a spectral database respectively to obtain the wave band with water color abnormality;
s400: comparing the pixel value of each position of each wave band with the water color anomaly to obtain a pixel with the water color anomaly;
s500: extracting the boundary of the pixel with the water color anomaly to obtain the distribution position and range of the water color anomaly;
s600: counting the number of pixels with abnormal water color, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels;
the method for determining the reference characteristic value and the pixel characteristic value comprises the following steps:
s100': acquiring multi-scene remote sensing image samples of different time sequences of a monitoring area, and storing the multi-scene remote sensing image samples into a spectral database;
s200': preprocessing the multi-scene remote sensing image sample to obtain a remote sensing reflectivity image of a normal water color part of the water body corresponding to the multi-scene remote sensing image sample;
s300': performing statistical analysis on each wave band of the remote sensing reflectivity image of the normal water color part of the water body corresponding to the multi-scene remote sensing image sample to obtain a reference characteristic value of each wave band and a pixel characteristic value of each position of each wave band;
the S300' includes:
s310': performing principal component analysis on a first wave band of a remote sensing reflectivity image of a normal water color part of a water body corresponding to the multi-scene remote sensing image sample to obtain a first principal component of the multi-scene remote sensing image sample in the first wave band;
s320': repeating S310' to obtain a first principal component of the multi-scene remote sensing image sample in each wave band;
s330': determining a reference characteristic value of each wave band and a pixel characteristic value of each position of each wave band according to the first principal component of each wave band;
the wave band characteristic value of each wave band is the average value of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected in the wave band, the reference characteristic value of each wave band is the product of the average value of the remote sensing reflectivity image of the water body water color normal part corresponding to all the remote sensing image samples in the wave band and the characteristic value of the first main component of the wave band, and the pixel characteristic value of each position of each wave band is the product of the pixel average value of the remote sensing reflectivity image of the water body water color normal part corresponding to all the remote sensing image samples in the wave band in the position and the characteristic value of the first main component of the wave band.
2. The method for identifying the water color anomaly based on the time sequence remote sensing image according to claim 1, wherein the method for determining the reference characteristic value and the pixel characteristic value further comprises the following steps:
s400': adding new remote sensing image samples of a plurality of scene monitoring areas into the spectral database, and executing S200 'and S300' to obtain updated reference characteristic values of each wave band and pixel characteristic values of each position of each wave band.
3. The method for identifying the water color anomaly based on the time series remote sensing image according to claim 1 or 2, wherein after S200 and before S300, the method further comprises:
s210: carrying out edge mask processing on the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected;
after S200 ', before S300', the method further includes: and carrying out edge mask processing on the remote sensing reflectivity image of the normal water color part of the water body corresponding to the multi-scene remote sensing image sample.
4. The method for identifying the water color anomaly based on the time sequence remote sensing image according to claim 3, wherein the remote sensing image to be detected and the remote sensing image sample are multispectral remote sensing images or hyperspectral remote sensing images;
the pre-processing includes radiation correction, atmospheric correction, cloud removal processing, and water-land separation.
5. The method for identifying the water color anomaly based on the time series remote sensing image according to claim 4, wherein after S400 and before S500, the method further comprises:
s410: marking the pixels with abnormal water color as 1, and marking the pixels without abnormal water color as 0 to obtain a binary image;
s500 further comprises: extracting the boundary of the part marked as 1 of the binary image to obtain the distribution position and range of the water color abnormality;
s600 further is: and counting the number of the pixels marked as 1 in the binary image, and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels.
6. The utility model provides a water body water color anomaly recognition device based on chronogenesis remote sensing image which characterized in that, the device includes:
the first acquisition module is used for acquiring a remote sensing image to be detected;
the first preprocessing module is used for preprocessing the remote sensing image to be detected to obtain a remote sensing reflectivity image of a water area part corresponding to the remote sensing image to be detected;
the wave band comparison module is used for calculating a wave band characteristic value of each wave band of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected, and comparing the wave band characteristic value of each wave band with a reference characteristic value of the corresponding wave band in the spectral database respectively to obtain the wave band with abnormal water color;
the pixel comparison module is used for comparing the pixel value of each position of each wave band with the water color abnormity with the pixel characteristic value of the corresponding position of the corresponding wave band in the spectrum database to obtain the pixel with the water color abnormity;
the boundary extraction module is used for extracting the boundary of the pixel with the water color anomaly to obtain the distribution position and range of the water color anomaly;
the area calculation module is used for counting the number of the pixels with abnormal water color and calculating the distribution area of the polluted water body according to the spatial resolution represented by the pixels;
the reference characteristic value and the pixel characteristic value are determined by the following modules:
the second acquisition module is used for acquiring multi-scene remote sensing image samples of different time sequences in a monitoring area and storing the multi-scene remote sensing image samples into a spectral database;
the second preprocessing module is used for preprocessing the multi-scene remote sensing image sample to obtain a remote sensing reflectivity image of a normal water color part of the water body corresponding to the multi-scene remote sensing image sample;
the statistical analysis module is used for performing statistical analysis on each wave band of the remote sensing reflectivity image of the water color normal part of the water body corresponding to the multi-scene remote sensing image sample to obtain a reference characteristic value of each wave band and a pixel characteristic value of each position of each wave band;
the statistical analysis module comprises:
the main component analysis unit is used for carrying out main component analysis on a first waveband of a remote sensing reflectivity image of a water color normal part of the water body corresponding to the multi-scene remote sensing image sample to obtain a first main component of the multi-scene remote sensing image sample in the first waveband;
the circulating unit is used for repeating the principal component analysis unit to obtain a first principal component of the multi-scene remote sensing image sample in each wave band;
a determining unit configured to determine a reference feature value of each band and a pixel feature value of each position of each band according to the first principal component of each band;
the wave band characteristic value of each wave band is the average value of the remote sensing reflectivity image of the water area part corresponding to the remote sensing image to be detected in the wave band, the reference characteristic value of each wave band is the product of the average value of the remote sensing reflectivity image of the water body water color normal part corresponding to all the remote sensing image samples in the wave band and the characteristic value of the first main component of the wave band, and the pixel characteristic value of each position of each wave band is the product of the pixel average value of the remote sensing reflectivity image of the water body water color normal part corresponding to all the remote sensing image samples in the wave band in the position and the characteristic value of the first main component of the wave band.
7. The water body water color abnormality recognition device based on the time series remote sensing image according to claim 6, characterized in that the device further comprises:
and the updating module is used for adding new remote sensing image samples of a plurality of scene monitoring areas into the spectral database, and executing the second preprocessing module and the statistical analysis module to obtain updated reference characteristic values of each wave band and pixel characteristic values of each position of each wave band.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010173305.8A CN111307727B (en) | 2020-03-13 | 2020-03-13 | Water body water color abnormity identification method and device based on time sequence remote sensing image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010173305.8A CN111307727B (en) | 2020-03-13 | 2020-03-13 | Water body water color abnormity identification method and device based on time sequence remote sensing image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111307727A CN111307727A (en) | 2020-06-19 |
| CN111307727B true CN111307727B (en) | 2020-10-30 |
Family
ID=71156939
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010173305.8A Active CN111307727B (en) | 2020-03-13 | 2020-03-13 | Water body water color abnormity identification method and device based on time sequence remote sensing image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111307727B (en) |
Families Citing this family (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111398176B (en) * | 2020-03-13 | 2020-11-20 | 生态环境部卫星环境应用中心 | Water body water color abnormity remote sensing identification method and device based on pixel scale characteristics |
| CN112035679B (en) * | 2020-09-10 | 2021-02-23 | 广东新禾道信息科技有限公司 | Data processing method and device for remote sensing monitoring natural disasters |
| CN112232234B (en) * | 2020-10-20 | 2021-04-16 | 生态环境部卫星环境应用中心 | Remote sensing-based method and device for evaluating cyanobacterial bloom strength in inland lakes and reservoirs |
| CN112927150B (en) * | 2021-02-20 | 2023-04-07 | 河北先河环保科技股份有限公司 | Water reflection area spectrum recovery method of hyperspectral image and terminal device |
| CN113140000A (en) * | 2021-03-26 | 2021-07-20 | 中国科学院东北地理与农业生态研究所 | Water body information estimation method based on satellite spectrum |
| CN112836181B (en) * | 2021-04-20 | 2021-08-31 | 中国水利水电科学研究院 | River light pollution index extraction method based on noctilucent remote sensing image |
| CN113177979B (en) * | 2021-06-04 | 2024-07-09 | 江苏南大五维电子科技有限公司 | Multispectral image-based water pollution area identification method and multispectral image-based water pollution area identification system |
| CN113536606A (en) * | 2021-09-15 | 2021-10-22 | 成都同飞科技有限责任公司 | Water quality change detection method and system, storage medium and device |
| CN113686788B (en) * | 2021-09-18 | 2022-08-09 | 重庆星视空间科技有限公司 | Conventional water quality monitoring system and method based on remote sensing wave band combination |
| CN115494013B (en) * | 2022-11-17 | 2023-03-24 | 河北先河环保科技股份有限公司 | Method and apparatus for detecting water quality abnormality and storage medium |
| CN116952873B (en) * | 2023-07-26 | 2024-08-23 | 重庆市科学技术研究院 | Method for measuring and calculating black and odorous degree of water body based on hyperspectral technology |
| CN117373024B (en) * | 2023-12-07 | 2024-03-08 | 潍坊市海洋发展研究院 | Method, device, electronic equipment and computer readable medium for generating annotation image |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006039689A (en) * | 2004-07-22 | 2006-02-09 | Nara Institute Of Science & Technology | Image processor, image processing method, image processing program, and recording medium with the program recorded thereon |
| CN105761286A (en) * | 2016-02-29 | 2016-07-13 | 环境保护部卫星环境应用中心 | Water color exception object extraction method and system based on multi-spectral remote sensing image |
| CN105761273B (en) * | 2016-03-18 | 2018-09-07 | 武汉大学 | A kind of abnormal target in hyperspectral remotely sensed image object detection method based on figure construction |
| CN106950197B (en) * | 2017-03-03 | 2017-12-08 | 环境保护部卫星环境应用中心 | The Remotely sensed acquisition methods, devices and systems of sewage draining exit polluted-water |
| CN106971146B (en) * | 2017-03-03 | 2018-04-03 | 环境保护部卫星环境应用中心 | Based on three water body exception remote sensing dynamic monitoring and controlling method, the device and system for looking into technology |
| CN106918559B (en) * | 2017-05-04 | 2019-03-12 | 环境保护部卫星环境应用中心 | A kind of method and device detecting Water quality |
-
2020
- 2020-03-13 CN CN202010173305.8A patent/CN111307727B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111307727A (en) | 2020-06-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111307727B (en) | Water body water color abnormity identification method and device based on time sequence remote sensing image | |
| US11836976B2 (en) | Method for recognizing seawater polluted area based on high-resolution remote sensing image and device | |
| CN111398176B (en) | Water body water color abnormity remote sensing identification method and device based on pixel scale characteristics | |
| CN110060237B (en) | Fault detection method, device, equipment and system | |
| CN110222672B (en) | Method, device and equipment for detecting wearing of safety helmet in construction site and storage medium | |
| CN110781756A (en) | Urban road extraction method and device based on remote sensing image | |
| CN114266773B (en) | Display panel defect positioning method, device, equipment and storage medium | |
| CA2840436A1 (en) | System for mapping and identification of plants using digital image processing and route generation | |
| CN111680704B (en) | Automatic and rapid extraction method and device for newly-increased human active plaque of ocean red line | |
| CN111739020B (en) | Automatic labeling method, device, equipment and medium for periodic texture background defect label | |
| CN113205511B (en) | Electronic component batch information detection method and system based on deep neural network | |
| CN114898097B (en) | Image recognition method and system | |
| CN113435407B (en) | Small target identification method and device for power transmission system | |
| CN116596875A (en) | Wafer defect detection method and device, electronic equipment and storage medium | |
| CN113033385A (en) | Deep learning-based violation building remote sensing identification method and system | |
| CN112131914B (en) | Lane line attribute detection method and device, electronic equipment and intelligent equipment | |
| CN113887472A (en) | Remote sensing image cloud detection method based on cascade color and texture feature attention | |
| CN112819753A (en) | Building change detection method and device, intelligent terminal and storage medium | |
| Acharya et al. | Deep neural network based approach for detection of defective solar cell | |
| CN113298755B (en) | Method and device for rapidly detecting ecological environment change patch based on time sequence image | |
| CN116630352A (en) | Rock core measurement method and device based on bidirectional cascade pixel differential network | |
| CN113284066B (en) | Automatic cloud detection method and device for remote sensing image | |
| CN115496976A (en) | Visual processing method, device, equipment and medium for multi-source heterogeneous data fusion | |
| CN113012137B (en) | Panel defect inspection method, system, terminal device and storage medium | |
| CN115601655A (en) | Water body information identification method and device based on satellite remote sensing and readable medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |