KR102446218B1 - Device for receiving and processing various types of satellite information on disaster safety - Google Patents

Abstract

The present invention relates to a device for receiving and processing various types of satellite information on disaster safety. According to the present invention, a device for receiving and processing various types of satellite information on disaster safety includes: satellite receiving antennas; receiving servers; image processing servers; and a satellite image storage and management server. It is possible to selectively receive and collect various types of satellite information related to disaster safety provided by various satellites, and predict disasters.

Description

Device for receiving and processing various types of satellite information on disaster safety}

The present invention selectively receives various types of satellite information related to disaster safety provided by various artificial satellites such as multi-purpose practical satellites, geostationary satellites, next-generation medium-sized satellites, and next-generation small satellites, and collects them after pre-processing to be utilized for the purpose of analysis of disaster safety. It relates to a disaster safety multi-satellite information receiving and processing device.

Earth observation satellite data are being used for various public purposes, such as disaster response, environmental and marine surveillance, and food security, or contributing to solving social problems. Accordingly, the Ministry of Science and ICT and related ministries established the '3rd Basic Plan for Space Development Promotion' in 2018 and established six key strategies for space development (space launch vehicle technical independence, satellite utilization service and development advancement, space exploration start, Korean satellite) It is promoting the establishment of a navigation system, creation of a space innovation ecosystem, nurturing of the space industry and creation of space jobs).

Earth observation satellites, subject to independent development under the '3rd Basic Plan for Space Development Promotion' and 'Medium and Long-Term Space Development Plan', are multi-purpose practical satellites that aim for ultra-precise observation of the Earth, It is divided into geostationary orbit satellites, next-generation medium-sized satellites aimed at observing land, forests, agriculture and water resources, and small satellites for scientific missions. When developing multi-purpose satellites and geostationary orbit satellites, the satellite development project focused on the development of satellite payloads and domestic weather monitoring systems. Then, in the development of medium and small satellites, the development of satellite data for the purpose of detecting and monitoring changes in the domestic surface of the earth began in earnest. focused on utilization. In the case of medium-sized satellites, it is possible to mount a highly usable payload by securing a standard projectile of about 500 kg, which makes it easy to analyze disasters occurring on the ground. It is known to be easy to use in

As of 2021 - Direct satellite image reception is being performed for Landsat 8 image, Terra satellite MODIS image, Aqua satellite MODIS image, and Suomi NPP satellite VIIRS image.

The Landsat 8 satellite is an earth observation satellite, equipped with two sensors, OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor). 11 bands of (ShortWave InfraRed) and TIR (Thermal InfraRed) wavelengths are provided.

MODIS, mounted on the Terra and Aqua satellites, is a multi-purpose sensor used for ocean, land, and atmospheric observation. 7), it has a spatial resolution of 1000 m (bands 8-36). Data obtained through each channel are analyzed and combined to produce data such as ocean, atmosphere, aerosol, surface condition and temperature.

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor mounted on the Suomi NPP (National Polar-orbiting Partnership) satellite carries out the subsequent mission of MODIS and has 22 channels in the visible and infrared wavelength bands. It consists of 16 Moderate bands with spatial resolution of 750 m, 5 Imagery bands with spatial resolution of 375 m, and Day/Night Band (DNB) with spatial resolution of 750 m. VIIRS has almost the same composition as MODIS, but has better spatial and temporal resolution, and has a Day/Night Band to provide nighttime images.

In addition, it will be able to receive various types of satellite information from satellites operating in Korea or scheduled to operate in the future.

In the prior art, satellite reception information is mainly used for broadcasting, communication, weather prediction, or topography observation, and is used for preparing for disaster safety, such as creating a heat distribution map, measuring ground displacement, and calculating the water surface area of a reservoir It is currently not being used.

Therefore, since the creation of a heat distribution map, measurement of ground displacement, and calculation of the surface area of a reservoir all depend on manual labor, a large number of workers and work time are required, the cost is high, and accurate prediction is impossible, so disaster analysis and preparation are done properly There was an unsustainable problem.

Patent Document 1: Korean Patent Publication No. 2000-0038273 Patent Document 2: Korean Patent Publication No. 2013-0007220

The present invention selectively receives various types of satellite information related to disaster safety provided by various artificial satellites such as multi-purpose practical satellites, geostationary satellites, next-generation medium-sized satellites, and next-generation small satellites, and collects them after pre-processing so that they can be utilized for the purpose of analysis of disaster safety. there is a purpose to

In other words, it can be used for disaster safety preparations such as creating a heat distribution map, measuring ground displacement, and calculating the water surface area of a reservoir by using various types of satellite information.

Another purpose is to strengthen disaster analysis and preparedness, as the number of workers and working time will be greatly reduced, cost will be low, and accurate prediction will be possible.

In an apparatus for receiving and processing disaster safety multi-species satellite information of the present invention for achieving the above object;

satellite receiving antennas; receiving servers for receiving satellite information respectively received from the antennas through a data interface and a satellite modem; image processing servers respectively connected to the receiving servers; It consists of a satellite image storage and management server for collecting satellite information transmitted from the image processing servers and providing it to administrators and users,

The satellite receiving antenna is composed of an antenna for receiving the Landsat 8 image and the Landsat 9 image of the US USGS, and an antenna for receiving the MODIS image and the VIIRS image of the US NASA,

The image received from Landsat 8,9 is connected to the data interface device through an optical cable, connected to a satellite modem through an RF cable, and transmitted to the Landsat 8,9 receiving server through LAN, and image processing in the Landsat 8,9 receiving server Data is sent over LAN to the server,

The image received from MODIS and VIIRS is connected to the data interface device through an optical cable, and is again transmitted to the MODIS/VIIRS receiving server through USB through the satellite modem, and from the MODIS/VIIRS receiving server to the image processing server through LAN data is sent,

Data from MODIS/VIIRS image processing server and Landsat 8,9 image processing server are transmitted to satellite image storage and management server,

Landsat 8,9 data is directly downlinked from the satellite through X-band communication, and L0R (Level 0 Reformatted), L1GT (Level 1 Systematic Terrain Corrected), L1TP (Level 1 Precision Terrain Corrected) processing is performed in the image processing server. is transmitted to the satellite image storage and management server through

MODIS data is directly received through X-band communication of DBS (Direct Broadcast System), and after processing Level 0 (Raw Instrument Packets), Level 1A (Scans of raw radiances in counts), Level 1B (Calibrated Radiances), etc., the satellite The image is stored and transmitted to the management server.

As the satellite image storage and management server, in addition to images directly received from Landsat 8 and 9, and MODIS and VIIRS, images received from Sentinel-1,2 through the Internet may be provided together.

The satellite information is received and processed by the program provided in the server, and the reception server is checked by the Acquisition and Director modules of the program.

The Acquisition module checks the reception status of Landsat 8, MODIS, and VIIRS, and the Director module is responsible for taking actions such as re-reception by analyzing the log,

Image processing server verification is also performed through the Director module, and satellite image storage and management server verification is performed through the Director module and NAS Manager module, and the Director module stores Landsat 8, Aqua MODIS, Terra MODIS, and VIIRS Checking the status, the NAS Manager module may be configured to manage storage usage information.

The images of Sentinel-1,2 are collected and pre-processed by a program provided in the server, and the images of Sentinel-1 can be configured such that download, radiation correction, terrain correction, RGB image generation, and KML generation are batch-processed. .

The images of Sentinel-1,2 are collected and pre-processed by a program provided in the server, and the images of Sentinel 2 may be configured such that download, atmospheric correction, RGB image generation, KML generation, and the like are batch-processed.

When the daemon program (S1_ver02_demon.py) in the server is executed, the sub program (S1_ver02_sub.py) is repeatedly executed according to the time interval set by the user. It is applied sequentially, and then it is linked with R to generate RGB images and KML files.

The Sentinel-1,2 images are downloaded according to the image search option set by the user using the Sentinel API library of Python, and the options necessary for throwing the API query from the parameter file are stored to download the images in bulk.

The preprocessing of the Sentinel-1 image is implemented by linking SNAP, a SAR image processing program provided in the server, and the SNAP's radial correction function (Calibration) and topography correction function (Ellipsoid-Correction-RD) are configured in XML files. and acquires Sentinel-1 satellite images (VV, VH) to which preprocessing is applied by calling the corresponding function using SNAP's Graphic Processing Tool (GPT).

The Sentinel-1 satellite image with radiation and topography correction completed is composed of VV and VH polarization. Using these band combinations, an RGB composite image is generated, and the histogram stretching technique is applied to express the RGB composite image more clearly. , The RGB composite image of Sentinel-1, whose histogram stretching technique has been applied, is created in KML format so that the Sentinel-1 image can be used in Google Earth.

When the daemon program (S2_ver02_demon.py) in the server is executed, the sub program (S2_ver02_sub .py) is repeatedly executed according to the interval set by the user, and the sub program is interlocked with ACOLITE to apply atmospheric correction of the image, and then It works with R to create the final output RGB image and KML format.

The pre-processing of the Sentinel-2 image is implemented in conjunction with ACOLITE, an atmospheric correction program provided in the server. ACOLITE is based on a Dark Spectrum Fitting (DSF) algorithm capable of atmospheric correction using a short-wave infrared (SWIR) wavelength band. Atmospheric correction is applied to Sentinel-2 Level-1C (L1C) image, which provides the reflectance of Top Of Atmosphere (TOA), which is the stage before atmospheric correction processing. A corresponding Bottom Of Atmosphere (BOA) image is calculated, and a Sentinel-2 image obtained by calculating the surface reflectivity is generated by correcting the radiant energy attenuated by scattering and absorption of the atmosphere.

Sentinel-2 satellite image with atmospheric correction completed is composed of VV and VH polarization. Using these band combinations, an RGB composite image is generated, and the histogram stretching technique is applied to express the RGB composite image more clearly, and histogram stretching. The RGB composite image of Sentinel-2 after the technique has been applied is created in KML format so that the Sentinel-2 image can be used in Google Earth.

In the disaster safety multi-type satellite information receiving and processing device according to the present invention,

There is an effect that can be used for disaster safety analysis purposes by selectively receiving and pre-processing various types of satellite information related to disaster safety provided by various artificial satellites such as multi-purpose practical satellites, geostationary satellites, next-generation medium-sized satellites, and next-generation small satellites. .

In other words, it can be used for disaster safety preparations such as creating a heat distribution map, measuring ground displacement, and calculating the water surface area of a reservoir by using various types of satellite information.

Since the number of workers and working time will be greatly reduced, the cost will be low, and accurate prediction will be possible, so it has the effect of strengthening disaster analysis and preparedness.

In addition, in the disaster safety multi-type satellite information receiving and processing apparatus according to the present invention, the USGS Landsat 8,9 satellite image and the US NASA MODIS image and VIIRS image are directly received, processed and stored for disaster safety analysis. Meanwhile,

Even the Sentinel-1/2 image received from the Internet can be used for disaster safety analysis because radiation correction, terrain correction, atmospheric correction, RGB image generation, and KML generation are batch-processed by the program provided in the image processing server. However, it also has the effect of enabling accurate analysis of a wider range of disasters.

1 is a block diagram showing a state in which an apparatus for receiving and processing disaster safety multi-type satellite information according to the present invention is connected to an external device;
2 is a schematic configuration diagram of an apparatus for receiving and processing disaster safety multi-type satellite information according to the present invention;
3 is a block diagram illustrating a process within a server in the device for receiving and processing disaster safety multi-species satellite information according to the present invention;
4 is a flowchart showing the Sentinel-1 collection and pre-processing (radiation/topography correction) automation procedure in the device for receiving and processing disaster safety multi-type satellite information according to the present invention;
5 is a flowchart showing the Sentinel-2 collection and pre-processing (atmospheric correction) automation procedure in the device for receiving and processing disaster safety multi-species satellite information according to the present invention;
6 is an exemplary diagram of GIS display of the Sentinel-1 satellite image in which radiation correction and topography correction have been applied in the device for receiving and processing disaster safety multi-type satellite information according to the present invention;
7 is an exemplary diagram of the RGB composite image generation process of RADARSAT-2 and Sentinel-1 in the disaster safety multi-type satellite information receiving and processing device according to the present invention;
8 is a diagram illustrating the final RGB composite image and KML format expression of Sentinel-1 in the disaster safety multi-type satellite information receiving and processing apparatus according to the present invention;
9 is an exemplary diagram of a process of applying atmospheric correction of a Sentinel-2 image in an apparatus for receiving and processing disaster safety multi-satellite information according to the present invention.
10 is an exemplary diagram showing the final RGB composite image and KML format of Sentinel-2 in the disaster safety multi-type satellite information receiving and processing apparatus according to the present invention.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings. However, it goes without saying that the scope of the present invention is not limited thereto.

In the present specification, the present embodiment is provided so that the disclosure of the present invention is complete, and is provided to completely inform those of ordinary skill in the art to which the present invention belongs, the scope of the invention, the scope of the present invention is only It is only defined by the claims. Thus, in some embodiments, well-known components, well-known operations, and well-known techniques have not been specifically described in order to avoid obscuring the present invention.

The terms used herein are for the purpose of describing the embodiments and are in no way intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. In addition, elements and operations referred to as 'include (or include)' do not exclude the presence or addition of one or more other elements and operations.

The present invention selectively receives various types of satellite information related to disaster safety provided by various artificial satellites such as multi-purpose practical satellites, geostationary satellites, next-generation medium-sized satellites, and next-generation small satellites, and collects them after pre-processing to be utilized for the purpose of analysis of disaster safety. It relates to a device for receiving and processing disaster safety multi-satellite information,

By selectively receiving satellite information received from Landsat 8,9 satellites, Terra and Aqua satellites, Suomi NPP (National Polar-orbiting Partnership) satellites, and Sentinel satellites, pre-processing and aggregating them, heat distribution map creation, ground displacement measurement, reservoir index It will be possible to provide data for disaster safety analysis and preparation, such as surface area calculation.

First, looking at the specifications and characteristics of the artificial satellite used in the present invention,

The Landsat 8 satellite was launched on February 11, 2013 as an earth observation satellite, and it is taking 740 satellite images every day at an altitude of 705 km, and revisiting the same area every 16 days. The Landsat 8 satellite is equipped with two sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Spatial resolution provides 11 bands of Visible and Near-InfraRed (VNIR), ShortWave InfraRed (SWIR), and Thermal InfraRed (TIR) wavelengths composed of 15, 30, and 100m.

The Terra and Aqua satellites are satellites launched by the Earth Observing System (EOS) operated mainly by the National Aeronautics and Space Administration (NASA). -2 times (Terra: around 10:30 a.m., Aqua: around 1:30 a.m.) Pass through the air around the Korean Peninsula. MODIS is a multi-purpose sensor that is mounted on Terra and Aqua satellites and used for ocean, land, and atmospheric observation. 7), it has a spatial resolution of 1000 m (bands 8-36). Data obtained through each channel are analyzed and combined to produce data such as ocean, atmosphere, aerosol, surface condition and temperature.

The Suomi NPP (National Polar-orbiting Partnership) satellite was launched in October 2011 by NOAA and NASA and is currently in operation. . The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor mounted on the Suomi NPP satellite performs the subsequent mission of MODIS and has 22 channels in the visible and infrared wavelength bands. It consists of 16 Moderate bands with spatial resolution of 750 m, 5 Imagery bands with spatial resolution of 375 m, and Day/Night Band (DNB) with spatial resolution of 750 m. VIIRS has almost the same composition as MODIS, but has better spatial and temporal resolution, and has a Day/Night Band to provide nighttime images.

Sentinel-1 is the first satellite of the Copernicus program satellite group conducted by the European Space Agency (ESA). The Sentinel-1 constellation consists of two satellites, Sentinel-1A (launched April 2014) and Sentinel-1B (launched April 2016), sharing the same orbital plane. Sentinel-1 A/B satellites operate in polar orbits, have a 12-day revisit cycle, and complete 175 orbits per cycle. The payload of the Sentinel-1 satellite is a C-band Synthetic Aperture Radar (SAR) device, capable of collecting data in all weather conditions, day and night. This equipment has a spatial resolution of less than 5 m and an observation width of up to 400 km, and provides 1 dB radiometric accuracy with a center frequency at 5,405 GHz.

The Sentinel-2 satellite was also developed as a Copernicus program conducted by the European Space Agency (ESA), performs earth surface monitoring, and consists of two polar orbiting satellites (A and B) that provide high-resolution optical images. It has a wide breadth (290 km) and a short revisit cycle (10 days with one satellite at the equator, 5 days with two satellites under cloudless conditions) for monitoring surface changes. The Sentinel-2 satellite is equipped with a multi-spectral instrument (MSI) and performs observations to detect changes in the surface of the earth and calculate geophysical variables, and can be used for climate change, surface monitoring, and disaster monitoring. The average observation time for land and coastal areas is about 17 minutes, and the maximum observation time is 32 minutes.

The Landsat 9 satellite is the latest satellite in the Landsat series, launched on September 27, 2021, is currently under maintenance, and is expected to begin normal operation in early January 2022. The satellite operates in a near-polar orbit and visits the same area every 16 days, providing satellite observation data necessary for global forest and urban change, water resource information, coral reef degradation, glacier and ice shelf retreat, natural disasters, and climate change. will do The Landsat 9 satellite has two payloads: OLI-2 (Operational Land Imager 2) and TIRS-2 (Thermal Infrared Sensor 2). It is 185 km. The TIRS-2 is a two-band thermal imaging sensor that measures the thermal infrared radiation of the earth's surface. Both instruments provide data in 15, 30, or 100 m resolution, depending on the spectral band.

1 is a block diagram showing a state in which an apparatus for receiving and processing disaster safety multi-type satellite information according to the present invention is connected to an external device;

an antenna for receiving information from satellites; It is composed of a server for selectively receiving various kinds of satellite information received through the antenna, pre-processing and collecting the information, and providing the collected satellite information to an administrator and a user, respectively.

2 is a schematic configuration diagram of an apparatus for receiving and processing disaster safety multi-type satellite information according to the present invention. The disaster-safe multi-type satellite information reception and processing apparatus according to the present invention includes: satellite receiving antennas; receiving servers for receiving satellite information respectively received from the antennas through a data interface and a satellite modem; image processing servers respectively connected to the receiving servers; It consists of a satellite image storage and management server connected to the image processing servers.

The satellite reception antenna may be composed of an antenna for receiving the Landsat 8 image and the Landsat 9 image of the US USGS, and an antenna for receiving the MODIS image and the VIIRS image of the US NASA,

The image received from Landsat 8,9 is connected to the data interface device through an optical cable, connected to the satellite modem through an RF cable, and transmitted to the Landsat 8,9 receiving server through LAN, and the image is processed by the Landsat 8,9 receiving server Data is transmitted over the LAN to the server.

On the other hand, the images received from MODIS and VIIRS are connected to the data interface equipment through an optical cable, and are again transmitted to the MODIS/VIIRS receiving server through USB through the satellite modem. data is transmitted through

The MODIS/VIIRS image processing server and Landsat 8/9 image processing server data are transmitted to the satellite image storage and management server, and the Sentinel-1,2 image is also received through the Internet and transmitted to the satellite image storage and management server. .

Landsat 8 data is downlinked directly from the satellite through X-band communication, and is processed by L0R (Level 0 Reformatted), L1GT (Level 1 Systematic Terrain Corrected), L1TP (Level 1 Precision Terrain Corrected), etc. in the image processing server. It is transmitted to the satellite image storage and management server.

MODIS data is directly received through X-band communication of DBS (Direct Broadcast System), and after processing Level 0 (Raw Instrument Packets), Level 1A (Scans of raw radiances in counts), Level 1B (Calibrated Radiances), etc., the satellite The image is stored and transmitted to the management server.

3 is a block diagram showing the processing process within the server in the disaster safety multi-type satellite information receiving and processing apparatus according to the present invention. .

The receiving server is checked by the FarEarth Acquisition and FarEarth Director modules. FarEarth Acquisition checks the reception status of Landsat 8, MODIS, and VIIRS, and the FarEarth Director is responsible for taking actions such as re-receiving by analyzing the log.

The image processing server verification is also performed through the FarEarth Director module.

The verification of the satellite image storage and management server is made through the FarEarth Director module and the NAS Manager module, the FarEarth Director checks the storage status of Landsat 8, Aqua MODIS, Terra MODIS, and VIIRS, and the NAS Manager manages the storage usage information do.

Currently, Sentinel-1/2 images are being collected through the Internet, but it is necessary to perform collection and pre-processing more effectively. Accordingly, a prototype of the collection and pre-processing automation module was developed. The program was written in Python and R, and Linux It can be used by registering it in crontab.

In Sentinel-1, download, radiation correction, terrain correction, RGB image generation, and KML generation are batch-processed, and Sentinel-2 is capable of batch processing such as download, atmospheric correction, RGB image generation, and KML generation. (See Figs. 4 and 5)

In the Sentinel-1 collection and preprocessing automation module, image download, radiation correction and topography correction linked with SNAP, and RGB image and KML format generation procedures are batch-processed.

When the daemon program (S1_ver02_demon.py) in the image processing server is executed, the sub program (S1_ver02_sub.py) is repeatedly executed according to the time interval set by the user. The sub-program works with SNAP to apply radial and topographical corrections on the image in turn, and then works with R to create RGB images and KML files.

To explain the video download process in detail,

Sentinel-1/2 video is downloaded according to the video search option set by the user using the Sentinel API library of Python. In this module, the options required to issue an API query are saved from a separately created parameter file, and the video batch download proceeds as follows.

Let me explain in detail about radiation correction and topography correction,

Sentinel-1 satellite image pre-processing is implemented by linking SNAP, a SAR image processing program provided in the image server. Sentinel-1 satellite image with preprocessing applied by composing SNAP's radiation correction function (Calibration) and topography correction function (Ellipsoid-Correction-RD) in XML file and calling the function using SNAP's Graphic Processing Tool (GPT) (VV, VH) is obtained.

To explain in detail the generation of RGB image and KML format,

Sentinel-1 satellite image after radiation correction and topography correction is composed of VV and VH polarization. Using the VV and VH polarization of RADARSAT-2 and Sentinel-1 images, RGB composite image is generated as follows. (See Fig. 7)

The histogram stretching technique can be applied to more clearly express the RGB composite image generated using the band combination of Sentinel-1 VV and VH polarization.

Histogram stretching is a method of extending the dynamic range of an image by redistributing the intensity intervals evenly in the histogram of the original image. As a result of applying histogram stretching to the intensity X _k of the input image, the intensity f(Xk) of the image is as follows.

where (L-1) is the maximum intensity of the output image, X _k is the input image of intensity k, and Xmax and Xmin are the maximum intensity and minimum intensity of the input image.

The RGB composite image of Sentinel-1, whose histogram stretching technique has been applied, can be generated in KML format so that the Sentinel-1 image can be used in Google Earth, etc.

Meanwhile, the Sentinel-2 collection and pre-processing automation module performs batch processing of image download, atmospheric correction in conjunction with ACOLITE, and generation of RGB image and KML format.

When the daemon program (S2_ver02_demon.py) in the image processing server is executed, the sub program (S2_ver02_sub.py) is repeatedly executed according to the interval set by the user. The sub-program works with ACOLITE to apply atmospheric correction as shown below, and then works with R to generate the final output RGB image and KML format.

The Sentinel-2 video download process is the same as the process in Sentinel-1.

On the other hand, let me explain in detail the Sentinel-2 atmospheric calibration process,

The pre-processing of Sentinel-2 satellite image is implemented in conjunction with ACOLITE, an atmospheric correction program provided in the image processing server. ACOLITE applies atmospheric correction based on the Dark Spectrum Fitting (DSF) algorithm, which can compensate the atmosphere using the short-wave infrared (SWIR) wavelength band.

The Sentinel-2 Level-1C (L1C) image provides the reflectance of the Top Of Atmosphere (TOA), which is the stage before atmospheric correction processing. Therefore, when the atmospheric correction process for TOA is performed with ACOLITE, the Bottom Of Atmosphere (BOA) corresponding to the surface reflectance image can be generated. Therefore, through this process, it is possible to generate a Sentinel-2 image in which the surface reflectivity is calculated by correcting the radiant energy attenuated by scattering and absorption of the atmosphere. (See Fig. 9)

For Sentinel-2 satellite image to which atmospheric correction is applied, RGB image is formed as in Sentinel-1, and KML format is created by applying histogram stretching technique. (See Fig. 10)

In the disaster safety multi-type satellite information receiving and processing device according to the present invention, the USGS Landsat 8,9 satellite image and the US NASA MODIS image and VIIRS image are directly received, processed and stored for disaster safety analysis,

Even the Sentinel-1/2 image received from the Internet can be used for disaster safety analysis because radiation correction, terrain correction, atmospheric correction, RGB image generation, and KML generation are batch-processed by the program provided in the image processing server. , accurate analysis of a wider range of disasters becomes possible.

In other words, it is very suitable for disaster safety analysis purposes by selectively receiving and pre-processing various types of satellite information related to disaster safety provided by various artificial satellites such as multi-purpose practical satellites, geostationary satellites, next-generation medium-sized satellites, and next-generation small satellites. It is effective and can be used for disaster safety preparations such as creating a heat distribution map, measuring ground displacement, and calculating the surface area of a reservoir by using various types of satellite information. Since it will be possible to predict, it also has the effect of strengthening disaster analysis and preparedness.

Although the technical idea of the present invention has been specifically described in the preferred embodiment, it should be noted that the above-described embodiment is for the description and not the limitation. It is obvious to those skilled in the art that various modifications and variations are possible within the scope of the technical spirit of the present invention, and therefore, it is natural that such variations and modifications belong to the appended claims.

Claims (12)

satellite receiving antennas; receiving servers for receiving satellite information respectively received from the antennas through a data interface and a satellite modem; image processing servers respectively connected to the receiving servers; It consists of a satellite image storage and management server for collecting satellite information transmitted from the image processing servers and providing it to administrators and users,
The satellite receiving antenna is composed of an antenna for receiving the Landsat 8 image and the Landsat 9 image of the US USGS, and an antenna for receiving the MODIS image and the VIIRS image of the US NASA,
The image received from Landsat 8,9 is connected to the data interface device through an optical cable, connected to a satellite modem through an RF cable, and transmitted to the Landsat 8,9 receiving server through LAN, and image processing in the Landsat 8,9 receiving server Data is sent over LAN to the server,
The image received from MODIS and VIIRS is connected to the data interface device through an optical cable, and is again transmitted to the MODIS/VIIRS receiving server through USB through the satellite modem, and from the MODIS/VIIRS receiving server to the image processing server through LAN data is sent,
Data from MODIS/VIIRS image processing server and Landsat 8,9 image processing server are transmitted to satellite image storage and management server,
Landsat 8,9 data is directly downlinked from the satellite through X-band communication, and L0R (Level 0 Reformatted), L1GT (Level 1 Systematic Terrain Corrected), L1TP (Level 1 Precision Terrain Corrected) processing is performed in the image processing server. is transmitted to the satellite image storage and management server through
MODIS data is directly received through X-band communication of DBS (Direct Broadcast System), and after processing Level 0 (Raw Instrument Packets), Level 1A (Scans of raw radiances in counts), Level 1B (Calibrated Radiances), etc., the satellite Disaster safety multi-variety satellite information receiving and processing device, characterized in that it is transmitted to the image storage and management server.
The method of claim 1,
As the satellite image storage and management server, in addition to the images directly received from the Landsat 8 and 9 and the MODIS and VIIRS, images received from Sentinel-1,2 through the Internet are provided together. Satellite information receiving and processing device.
3. The method of claim 1 or 2,
The satellite information is received and processed by the program provided in the server, and the reception server is checked by the Acquisition and Director modules of the program.
The Acquisition module checks the reception status of Landsat 8, MODIS, and VIIRS, and the Director module is responsible for taking actions such as re-reception by analyzing the log,
Image processing server verification is also performed through the Director module, and satellite image storage and management server verification is performed through the Director module and NAS Manager module, and the Director module stores Landsat 8, Aqua MODIS, Terra MODIS, and VIIRS Disaster safety multi-type satellite information receiving and processing device, characterized in that it checks the status, and the NAS Manager module is configured to manage storage usage information.
3. The method of claim 2,
The images of Sentinel-1,2 are collected and pre-processed by a program provided in the server, and the images of Sentinel-1 are configured such that download, radiation correction, terrain correction, RGB image generation, and KML generation are batch-processed. Disaster and safety multi-satellite receiving and processing device.
3. The method of claim 2,
Disaster safety, characterized in that the images of Sentinel-1,2 are collected and pre-processed by a program provided in the server. A device for receiving and processing various types of satellite information.
5. The method of claim 4,
When the daemon program (S1_ver02_demon.py) in the server is executed, the sub program (S1_ver02_sub.py) is repeatedly executed according to the time interval set by the user. Disaster safety multi-species satellite information receiving and processing device, characterized in that it is sequentially applied, and then interlocked with R to generate RGB images and KML files.
6. The method of claim 4 or 5,
The Sentinel-1,2 video is downloaded according to the video search option set by the user using the Sentinel API library of Python, and the video is downloaded in bulk by storing the options required to throw the API query from the parameter file. Disaster safety multi-satellite information receiving and processing device.
5. The method of claim 4,
The preprocessing of the Sentinel-1 image is implemented by linking SNAP, a SAR image processing program provided in the server, and the SNAP's radial correction function (Calibration) and topography correction function (Ellipsoid-Correction-RD) are configured in XML files. and, by calling the corresponding function using SNAP's Graphic Processing Tool (GPT), the pre-processed Sentinel-1 satellite image (VV, VH) is obtained.
5. The method of claim 4,
The Sentinel-1 satellite image with radiation and topography correction completed is composed of VV and VH polarization. Using these band combinations, an RGB composite image is generated, and the histogram stretching technique is applied to express the RGB composite image more clearly. , A device for receiving and processing various types of disaster and safety satellite information, characterized in that the RGB composite image of Sentinel-1, whose histogram stretching technique has been applied, is generated in KML format so that the Sentinel-1 image can be used in Google Earth.
6. The method of claim 5,
When the daemon program (S2_ver02_demon.py) in the server is executed, the sub program (S2_ver02_sub .py) is repeatedly executed according to the interval set by the user, and the sub program is interlocked with ACOLITE to apply atmospheric correction of the image, and then Disaster safety multi-satellite information receiving and processing device, characterized in that it is linked with R to generate the final output, RGB image and KML format.
6. The method of claim 5,
The pre-processing of the Sentinel-2 image is implemented in conjunction with ACOLITE, an atmospheric correction program provided in the server. ACOLITE is based on a Dark Spectrum Fitting (DSF) algorithm capable of atmospheric correction using a short-wave infrared (SWIR) wavelength band. Atmospheric correction is applied to Sentinel-2 Level-1C (L1C) image, which provides the reflectance of Top Of Atmosphere (TOA), which is the stage before atmospheric correction processing. The corresponding Bottom Of Atmosphere (BOA) image is calculated, and according to this, a Sentinel-2 image is generated from which the surface reflectivity is calculated by correcting the radiant energy attenuated by scattering and absorption of the atmosphere. Receiving and processing units.
6. The method of claim 5,
Sentinel-2 satellite image with atmospheric correction completed is composed of VV and VH polarization. Using these band combinations, an RGB composite image is generated, and the histogram stretching technique is applied to express the RGB composite image more clearly, and histogram stretching. Disaster safety multi-satellite information receiving and processing device, characterized in that the RGB composite image of Sentinel-2, whose technique has been applied, is generated in KML format so that the Sentinel-2 image can be used in Google Earth.

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