CN114785965A - Hyperspectral image automatic exposure method and system based on COPOD algorithm - Google Patents
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Abstract
The invention discloses a hyperspectral image automatic exposure method and a hyperspectral image automatic exposure system based on a COPOD algorithm, which relate to the technical field of image preprocessing and have the technical scheme key points that: acquiring a hyperspectral original image with exposure time; calculating an image characteristic vector set in each hyperspectral original image by taking the K-order moment as a main part; calculating a bilateral experience cumulative distribution function based on the image feature vector set; calculating an empirical Copula function based on the two-side empirical cumulative distribution function; estimating probability values of two tail ends jointly distributed on all dimensions through an empirical Copula function; analyzing according to the probability of the tail ends at two sides to obtain the analysis result of overexposure, over darkness or normal exposure of the hyperspectral original image; and adjusting the exposure time according to the abnormal exposure condition until the exposure is normal. The method can ensure good accuracy and generalization by utilizing the characteristic vector of the original image and the empirical Coupli function.
Description
Technical Field
The invention relates to the technical field of image preprocessing, in particular to a hyperspectral image automatic exposure method and system based on a COPOD algorithm.
Background
The hyperspectral camera can fully exert the advantage of high resolution in the spectral dimension only under the condition of normal exposure. Due to the challenges of long imaging time, large memory of output images and the like, the method for optimizing the imaging effect by manually adjusting the exposure time of the camera according to the sampling environment is backward and cumbersome. Most of the existing automatic exposure algorithms are based on traditional RGB cameras and are difficult to apply to hyperspectral cameras. Therefore, how to research and design a hyperspectral image automatic exposure method and system based on the COPOD algorithm which can overcome the defects is a problem which is urgently needed to be solved at present.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a hyperspectral image automatic exposure method and system based on a COPOD algorithm, which can efficiently complete the automatic exposure function of a hyperspectral camera by utilizing the feature vectors corresponding to original images under different exposure time, and can ensure good accuracy and generalization by utilizing the feature vectors and the empirical Coupl function of the original images.
The technical purpose of the invention is realized by the following technical scheme:
in a first aspect, a hyperspectral image automatic exposure method based on a COPOD algorithm is provided, which comprises the following steps:
acquiring a hyperspectral original image with exposure time;
calculating an image characteristic vector set in each hyperspectral original image by taking a K-order moment as a main part;
calculating a bilateral empirical cumulative distribution function based on the image feature vector set;
calculating an empirical Copula function based on the two-side empirical cumulative distribution function;
estimating probability values of two tail ends jointly distributed on all dimensions through an empirical Copula function;
analyzing according to the probability of the tail ends at two sides to obtain the analysis result of overexposure, over-darkness or normal exposure of the hyperspectral original image;
and adjusting the exposure time according to the abnormal exposure condition until the exposure is normal.
Further, the process of acquiring the hyperspectral original image specifically comprises:
acquiring an original hyperspectral image with exposure time in a line scanning mode or a snapshot mode by adopting a hyperspectral camera;
cutting an original hyperspectral image into images with uniform height, width and spectral channel number;
and naming the cut image in a unified format and recording corresponding exposure time to obtain the hyperspectral original image.
Further, the calculation process of the image feature vector set specifically includes:
and calculating an index set corresponding to the pixel value statistics by taking the K-order moment as a main mode band by band.
The pixel value statistics include: the first order origin moment, the second order center moment, the third order center distance, the fourth order center moment, the entropy value and the pixel proportion of which the brightness is larger than a given value of each channel pixel value.
Further, the formula for calculating the entropy value is specifically as follows:
wherein X represents a random variable of a pixel gray value of a certain frame of the hyperspectral image; entropy (X) represents the entropy value of a random variable X; xiRepresenting the actual value of the ith sample; p (X ═ X)i) Representing a sample value of XiThe frequency of (c); n represents the total number of samples; n represents the amount of sample taken.
Furthermore, the pixel value statistic also comprises a combination statistic consisting of at least two of a first-order origin moment, a second-order central moment, a third-order central distance, a fourth-order central moment, an entropy value and a pixel proportion with the brightness larger than a given value.
Further, the calculation process of the two-sided empirical cumulative distribution function specifically includes:
and calculating a left cumulative probability density function, wherein the calculation formula is specifically as follows:
and calculating a right cumulative probability density function, wherein the calculation formula is as follows:
calculating the skewness coefficient, wherein the calculation formula specifically comprises the following steps:
wherein ,
a left cumulative probability density function representing the d-th dimensional feature;
a right cumulative probability density function representing the d-th dimension feature; b is a mixture ofdA skewness coefficient representing the d-dimensional feature; x represents the order statistic of the sample; xiRepresenting the actual value of the ith sample;
represents the sample mean; n represents the amount of sample taken.
Further, the calculation process of the empirical Copula function specifically includes:
calculating left-side empirical Copula observation according to the hyperspectral image sample:
calculating the right from the hyperspectral image sampleSide experience Copula observations:
calculating Copula observation statistics: if b isdIf less than 0, then
If not, then the mobile terminal can be switched to the normal mode,
wherein ,
represents the left empirical value;
represents the right empirical value;
represents Copula observations; x is a radical of a fluorine atomiRepresenting an order statistic of ordinal number i; b is a mixture ofdA skewness coefficient representing the d-th dimension characteristic;
representing observed statistics.
Further, the calculation process of the probability values of the two tail ends specifically includes:
calculating the probability of the left tail according to the hyperspectral image samples:
calculating the probability of the right tail according to the hyperspectral image samples:
calculating the integral tail probability according to the hyperspectral image sample:
wherein ,plRepresenting the left tail probability; p is a radical ofrRepresenting the right tail probability; p is a radical ofsRepresenting the overall tail probability; d represents a dimension characteristic sequence number;
a left empirical value representing a j-th dimension feature;
right empirical values representing features of dimension j; represent
Observation statistics of the j-th dimension feature.
Further, the obtaining process of the analysis result specifically comprises:
obtaining a judgment index score according to the probability value analysis of the two-side tail ends, wherein the calculation formula of the index score is as follows:
wherein ,S(xi) Indicating a determination index score; p is a radical oflRepresenting the left tail probability; p is a radical ofrRepresenting the right tail probability; p is a radical ofsRepresenting the overall tail probability;
and repeatedly calculating to obtain the judging index scores of all samples, and analyzing and judging the overexposure, the over-darkness or the normal exposure of the hyperspectral original image according to the distribution interval of the judging index scores.
In a second aspect, a hyperspectral image automatic exposure system based on a COPOD algorithm is provided, which includes:
the image acquisition module is used for acquiring a hyperspectral original image with exposure time;
the characteristic calculation module is used for calculating an image characteristic vector set in each hyperspectral original image by taking the K-order moment as a main part;
the first function module is used for calculating a bilateral empirical cumulative distribution function based on the image feature vector set;
the second function module is used for calculating an empirical Copula function based on the two-side empirical cumulative distribution function;
the probability estimation module is used for estimating probability values of the two tail ends jointly distributed on all dimensions through an empirical Copula function;
the result analysis module is used for obtaining analysis results of overexposure, over-darkness or normal exposure of the hyperspectral original image according to probability analysis of the tail ends at two sides;
and the automatic adjusting module is used for adjusting the exposure time according to the abnormal exposure condition until the exposure is normal.
Compared with the prior art, the invention has the following beneficial effects:
1. compared with the traditional visible light image automatic exposure, the hyperspectral image automatic exposure method based on the COPOD algorithm provided by the invention has the characteristics that the hyperspectral image automatic exposure relates to a plurality of image spectral bands, large data volume, obvious imaging difference of different spectral bands and the like; according to the invention, the automatic exposure function of the hyperspectral camera is efficiently completed by utilizing the characteristic vectors corresponding to the original images under different exposure times, and good accuracy and generalization can be ensured by utilizing the characteristic vectors of the original images and the empirical Coipla function;
2. in the application process, extra detection equipment and detection equipment are not required to be added, the exposure time can be automatically adjusted by combining the returned result, the automatic exposure function is realized, and the method has strong adaptability and practicability for the application scene.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart in an embodiment of the invention;
FIG. 2 is a probability density chart in an embodiment of the present invention, where a is skewness, b is entropy, c is a pixel fraction with a pixel value lower than 10, d is a pixel fraction with a pixel value higher than 200, and e is a ratio of standard deviation to mean;
FIG. 3 is a graph of probability densities, where a is the mean, b is the standard deviation, and c is the kurtosis value, in an embodiment of the present invention;
FIG. 4 is a graph of probability density for an embodiment of the invention, where a is a distribution plot of the proportion of pixel values above 253, and b is a distribution plot of the proportion of pixel values below 4;
FIG. 5 is a diagram showing the results of the detection in the embodiment of the present invention;
fig. 6 is a block diagram of a system in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and the accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not used as limiting the present invention.
Example 1: a hyperspectral image automatic exposure method based on a COPOD algorithm is shown in figure 1 and comprises the following steps:
s1: acquiring a hyperspectral original image with exposure time;
s2: calculating an image characteristic vector set in each hyperspectral original image by taking the K-order moment as a main part;
s3: calculating a bilateral empirical cumulative distribution function based on the image feature vector set;
s4: calculating an empirical Copula function based on the two-side empirical cumulative distribution function;
s5: estimating probability values of the two-side tail ends jointly distributed on all dimensions through an empirical Copula function;
s6: analyzing according to the probability of the tail ends at two sides to obtain the analysis result of overexposure, over-darkness or normal exposure of the hyperspectral original image;
s7: and adjusting the exposure time according to the abnormal exposure condition until the exposure is normal.
It should be noted that the hyperspectral original image can also be replaced by a multispectral original image, and the technology provided by the invention is also applicable.
In step S1, the process of acquiring the hyperspectral original image specifically includes: acquiring an original hyperspectral image with exposure time in a line scanning mode or a snapshot mode by adopting a hyperspectral camera; cutting an original hyperspectral image into images with uniform height, width and spectral channel number; and naming the cut image in a unified format and recording corresponding exposure time to obtain the hyperspectral original image.
For example, a picture cropped to h × w × c, which is the height, width and number of spectral channels, respectively, may be named in a way that the picture name contains its exposure time field.
The image feature vector set calculation process specifically includes: and calculating an index set corresponding to the pixel value statistics by taking the K-order moment as a main mode band by band. The pixel value statistics include: the first-order origin moment, the second-order middle i moment, the third-order center distance, the fourth-order center moment, the entropy value and the pixel proportion of which the brightness is larger than a given value of each channel pixel value.
The moment statistic is the important statistic of a random variable distribution function, and the definition of the K-order central moment of a sample distribution is as follows:
the integral form is:
x represents a random variable of a pixel gray value of a certain frame of the hyperspectral image; xiActually taking a value for a sample, namely taking a value for the gray level of a specific pixel of a certain frame of a hyperspectral image; f (x) is a probability density function of the pixel gray value distribution. The moment is used as an important and basic statistic for measuring the properties of the distribution function, so that corresponding indexes are obtained by calculation according to the definition of the moment statistic, and the properties and the characteristics of different types of images can be reflected.
The first order origin moment, also called the mean, is expressed as:
the second central moment, also called the variance, is expressed as:
the third-order center distance, also called skewness, is expressed as:
the fourth order central moment, also called kurtosis, is expressed as:
the formula for calculating the entropy value is specifically as follows:
wherein X represents a random variable of a pixel gray value of a certain frame of the hyperspectral image; entropy (X) represents the entropy value of a random variable X; xiRepresenting the actual value of the ith sample; p (X ═ X)i) Representing a sample taking the value XiThe frequency of (c); n represents the total number of samples; n represents the amount of sample taken.
The pixel value statistic also comprises a combination statistic consisting of at least two of a first-order origin moment, a second-order central moment, a third-order central distance, a fourth-order central moment, an entropy value and a pixel proportion with the brightness larger than a given value, and can also be a transformation statistic.
For example, the combined statistics of mean divided by standard deviation are more sensitive as indicators. The standard deviation is the square root of the variance of the samples, which is defined as:
the mean-standard deviation ratio is a combination index and has the expression：
Where μ above represents the mean and δ represents the standard deviation.
The pixel proportion with excessively high gray level is defined as the percentage of pixel gray level value of a certain frame in the hyperspectral image exceeding a certain threshold, and the expression is as follows:
wherein a is a threshold value.
Using the existing hyperspectral image samples, the indexes corresponding to the statistics of each item in step S2 are calculated, and probability density maps of the calculated indexes are obtained as shown in fig. 2, fig. 3, and fig. 4.
The calculation process of the two-sided empirical cumulative distribution function specifically comprises the following steps:
(1) and calculating a left cumulative probability density function, wherein the calculation formula is as follows:
(2) and calculating a right cumulative probability density function, wherein the calculation formula is as follows:
(3) calculating the skewness coefficient, wherein the calculation formula specifically comprises the following steps:
wherein ,
a left cumulative probability density function representing the d-th dimension feature;
representing d-th dimensional featuresRight cumulative probability density function of (1); bd represents the skewness coefficient of the d-dimension feature; x represents the order statistic of the sample; xi represents the actual value of the ith sample;
represents the sample mean; n represents the amount of sample taken.
The calculation process of the empirical Copula function specifically comprises the following steps:
(1) calculating left-side empirical Copula observation according to the hyperspectral image sample:
(2) calculating right-side empirical Copula observation according to the hyperspectral image sample:
(3) calculating Copula observation statistics: if bd<0, then
If not, then,
wherein ,
represents the left empirical value;
represents the right empirical value;
represents Copula observations; xi represents the order statistic with the order i; bd represents the skewness coefficient of the d-dimension feature;
representing observed statistics.
The calculation process of the probability values of the two tail ends is specifically as follows:
(1) calculating the probability of the left tail according to the hyperspectral image sample:
(2) calculating the probability of the right tail according to the hyperspectral image samples:
(3) calculating the integral tail probability according to the hyperspectral image sample:
wherein ,plRepresenting the left tail probability; p is a radical of formularRepresenting the right tail probability; p is a radical ofsRepresenting the overall tail probability; d represents a dimension characteristic sequence number;
a left empirical value representing a j-th dimension characteristic;
right empirical values representing features of dimension j; to represent
Observation statistics of the jth dimensional feature.
The process of obtaining the analysis result specifically comprises the following steps:
(1) and obtaining a judgment index score according to the probability value analysis of the two tail ends, wherein the calculation formula of the index score is as follows:
wherein ,S(xi) Indicating a determination index score; p is a radical oflRepresenting the left tail probability; p is a radical ofrRepresenting the right tail probability; p is a radical ofsRepresenting the overall tail probability;
(2) and repeatedly calculating to obtain the judgment index scores of all samples, and analyzing and judging the overexposure, the over-darkness or the normal exposure of the hyperspectral original image according to the distribution interval of the judgment index scores.
The detection results of hyperspectral images at different exposure times by using the COPOD anomaly detection algorithm are shown in fig. 5. Wherein the value 1 of the longitudinal axis represents abnormal exposure, and the value 2 represents normal exposure; the horizontal axis is exposure time, the left side anomalies are underexposed overly dark samples, and the right side anomalies are overexposed samples.
Example 2: the hyperspectral image automatic exposure system based on the COPOD algorithm comprises an image acquisition module, a feature calculation module, a first function module, a second function module, a probability estimation module, a result analysis module and an automatic adjustment module, as shown in FIG. 6.
The image acquisition module is used for acquiring a hyperspectral original image with exposure time; the characteristic calculation module is used for calculating an image characteristic vector set in each hyperspectral original image by taking the K-order moment as a main part; the first function module is used for calculating a bilateral experience cumulative distribution function based on the image feature vector set; the second function module is used for calculating an empirical Copula function based on the two-side empirical cumulative distribution function; the probability estimation module is used for estimating probability values of the two tail ends jointly distributed on all dimensions through an empirical Copula function; the result analysis module is used for obtaining the analysis results of overexposure, overexposure or normal exposure of the hyperspectral original image according to probability analysis of the tail ends at two sides; and the automatic adjusting module is used for adjusting the exposure time according to the abnormal exposure condition until the exposure is normal.
The working principle is as follows: compared with the traditional automatic exposure of a visible light image, the automatic exposure of the hyperspectral image relates to the characteristics of numerous image spectrums, large data volume, obvious imaging difference of different spectrums and the like; according to the invention, the automatic exposure function of the hyperspectral camera is efficiently completed by utilizing the characteristic vectors corresponding to the original images under different exposure times, and good accuracy and generalization can be ensured by utilizing the characteristic vectors of the original images and the empirical Coipla function; in addition, in the application process, extra detection equipment and detection equipment are not required to be added, the exposure time can be automatically adjusted by combining the returned result, the automatic exposure function is realized, and the method has strong adaptability and practicability for the application scene.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A hyperspectral image automatic exposure method based on a COPOD algorithm is characterized by comprising the following steps:
acquiring a hyperspectral original image with exposure time;
calculating an image characteristic vector set in each hyperspectral original image by taking the K-order moment as a main part;
calculating a bilateral experience cumulative distribution function based on the image feature vector set;
calculating an empirical Copula function based on the two-side empirical cumulative distribution function;
estimating probability values of the two-side tail ends jointly distributed on all dimensions through an empirical Copula function;
analyzing according to the probability of the tail ends at two sides to obtain the analysis result of overexposure, over-darkness or normal exposure of the hyperspectral original image;
and adjusting the exposure time according to the abnormal exposure condition until the exposure is normal.
2. The COPOD algorithm-based hyperspectral image automatic exposure method according to claim 1, wherein the hyperspectral original image acquisition process specifically comprises:
acquiring an original hyperspectral image with exposure time in a line scanning mode or a snapshot mode by adopting a hyperspectral camera;
cutting an original hyperspectral image into images with uniform height, width and spectral channel number;
and naming the cut image in a unified format and recording corresponding exposure time to obtain the hyperspectral original image.
3. The COPOD algorithm-based hyperspectral image automatic exposure method according to claim 1, wherein the image feature vector set is calculated by:
and calculating an index set corresponding to the pixel value statistic by taking the K-order moment as a main step and wave band by step.
The pixel value statistics include: the first order origin moment, the second order center moment, the third order center distance, the fourth order center moment, the entropy value and the pixel proportion of which the brightness is larger than a given value of each channel pixel value.
4. The method for automatically exposing hyperspectral images based on the COPOD algorithm according to claim 3, wherein the formula for calculating the entropy value is specifically as follows:
x represents a random variable of a pixel gray value of a certain frame of the hyperspectral image; entropy (X) represents the entropy value of a random variable X; xiRepresenting the actual value of the ith sample; p (X ═ X)i) Representing a sample taking the value XiThe frequency of (d); n represents the total number of samples; n represents the amount of sample taken.
5. The COPOD algorithm-based hyperspectral image automatic exposure method according to claim 3 wherein the pixel value statistics further comprise a combined statistic consisting of at least two of a first order origin moment, a second order center moment, a third order center distance, a fourth order center moment, an entropy value, and a pixel proportion with a luminance greater than a given value.
6. The COPOD algorithm-based hyperspectral image automatic exposure method according to claim 1, wherein the calculation process of the bilateral empirical cumulative distribution function is specifically:
and calculating a left cumulative probability density function, wherein the calculation formula is as follows:
and calculating a right cumulative probability density function, wherein the calculation formula is as follows:
calculating skewness coefficient, wherein the calculation formula is as follows:
wherein ,
a left cumulative probability density function representing the d-th dimension feature;
a right cumulative probability density function representing the d-th dimensional feature; b is a mixture ofdA skewness coefficient representing the d-th dimension characteristic; x represents the order statistic of the sample; xiRepresenting the actual value of the ith sample;
represents the sample mean; n represents the amount of sample taken.
7. The COPOD algorithm-based hyperspectral image automatic exposure method according to claim 1, wherein the computation process of the empirical Copula function is specifically as follows:
calculating left-side empirical Copula observation according to the hyperspectral image sample:
calculating right-side empirical Copula observation according to the hyperspectral image sample:
calculating Copula observation statistics: if b isd<0, then
If not, then,
wherein ,
represents the left empirical value;
right-hand empirical values;
represents Copula observations; x is a radical of a fluorine atomiRepresenting a sequence statistic of number i; b is a mixture ofdA skewness coefficient representing the d-th dimension characteristic;
representing observed statistics.
8. The method for automatically exposing the hyperspectral image based on the COPOD algorithm according to claim 1, wherein the calculation process of the probability values of the two tail ends is specifically as follows:
calculating the left tail according to the hyperspectral image samplePartial probability:
calculating the probability of the right tail according to the hyperspectral image sample:
calculating the integral tail probability according to the hyperspectral image sample:
wherein ,plRepresenting the left tail probability; p is a radical of formularRepresenting the right tail probability; p is a radical ofsRepresenting the overall tail probability; d represents a dimension characteristic serial number;
a left empirical value representing a j-th dimension characteristic;
right empirical values representing features of dimension j; to represent
Observation statistics of the jth dimensional feature.
9. The COPOD algorithm-based hyperspectral image automatic exposure method according to claim 1, wherein the obtaining process of the analysis result is specifically:
and obtaining a judgment index score according to the probability value analysis of the two tail ends, wherein the calculation formula of the index score is as follows:
wherein ,S(xi) Indicating a determination index score; p is a radical oflRepresenting the left tail probability; p is a radical ofrRepresenting the right tail probability; p is a radical ofsRepresenting the overall tail probability;
and repeatedly calculating to obtain the judging index scores of all samples, and analyzing and judging the overexposure, the over-darkness or the normal exposure of the hyperspectral original image according to the distribution interval of the judging index scores.
10. A hyperspectral image automatic exposure system based on a COPOD algorithm is characterized by comprising:
the image acquisition module is used for acquiring a hyperspectral original image with exposure time;
the characteristic calculation module is used for calculating an image characteristic vector set in each hyperspectral original image by taking the K-order moment as a main part;
the first function module is used for calculating a bilateral empirical cumulative distribution function based on the image feature vector set;
the second function module is used for calculating an empirical Copula function based on the two-side empirical cumulative distribution function;
the probability estimation module is used for estimating probability values of the two tail ends jointly distributed on all dimensions through an empirical Copula function;
the result analysis module is used for obtaining analysis results of overexposure, over-darkness or normal exposure of the hyperspectral original image according to probability analysis of the tail ends at two sides;
and the automatic adjusting module is used for adjusting the exposure time according to the abnormal exposure condition until the exposure is normal.
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