CN115147728A - Method for rapidly identifying small reservoir based on cooperation of radar data and optical data - Google Patents
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Abstract
The invention discloses a method for quickly identifying a small reservoir based on radar data and optical data cooperation, which comprises the following steps of: step 1, acquiring image data of a satellite radar of the small reservoir and preprocessing the image data to obtain a corresponding time sequence backscattering coefficient of the image data of the satellite radar of the small reservoir; step 2, acquiring pond dam DEM data to remove mountain shadow parts in backscattering coefficients; establishing a water body sample ROI from the backscattering coefficient after the mountain shadow is removed, determining an optimal land and water segmentation threshold value based on the water body sample ROI, and performing land and water segmentation based on the optimal land and water segmentation threshold value to obtain crude extraction result data of the small reservoir; and 3, acquiring optical image data of the small reservoir satellite, calculating a normalized water body index NDWI based on the optical image data of the small reservoir satellite, selecting the maximum value of the NDWI, and finely correcting a coarse extraction result of the small reservoir to finally obtain fine extraction result data of the small reservoir. The invention can effectively and quickly identify the special water body.
Description
Technical Field
The invention relates to the field of remote sensing data identification methods, in particular to a method for quickly identifying a small reservoir based on the cooperation of radar data and optical data.
Background
The main water source support of the rain-farming agriculture plays an important role in adjusting agricultural production, living and water storage of various types of fields.
The space-time distribution characteristics of the small reservoir are of great significance to the monitoring and application of water resources, particularly in the upland area with complex terrain. Therefore, the extraction of the information of the small reservoir can better find the space-time distribution rule of the small reservoir, and provide necessary basic service for regular inspection, maintenance, technical transformation, water resource investigation, water resource macroscopic monitoring and emergency monitoring of the small reservoir hydraulic engineering. The traditional water body information extraction mainly comprises manual field measurement and data acquisition based on a hydrological monitoring station, and is long in periodicity, high in risk, time-consuming and labor-consuming, and not suitable for large-scale information acquisition and real-time monitoring of high space-time frequency. However, if the information of the small reservoir is obtained based on the point cloud data, the large-scale distribution rule of the point data is difficult to find, and the appearance of the small reservoir changes along with the change of time, so that the small reservoir is difficult to identify quickly.
The synthetic aperture radar SAR has unique advantages in the field of geoscience remote sensing as a representative of microwave remote sensing, breaks through the limitation of optical remote sensing on the influence of external conditions such as weather and the like, has all-weather, all-time and large-range working capacity, is rich in characteristic signals, contains various information such as amplitude, phase and polarization and the like, makes up the defects of common optical images, and is widely applied to the fields of resource detection, environment monitoring, military reconnaissance and the like. The sentinel-1 data can provide high-resolution radar images for free under any weather conditions and provide images all day long, so that a long-time-sequence data source is provided for the extraction of the space information of the small reservoir.
In summary, in view of the all-weather characteristic of SAR earth observation and the advantage that SAR is sensitive to water body extraction, the identification of the special water body of the small reservoir by using the time sequence sentinel-1 data is necessary and feasible.
Disclosure of Invention
The invention aims to provide a method for rapidly identifying an embankment based on cooperation of radar data and optical data, and the method is used for solving the problem that the information of the embankment in the prior art is difficult to dynamically monitor and extract.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for rapidly identifying a small reservoir based on radar data and optical data cooperation is used for rapidly identifying a special water body, namely the small reservoir, by utilizing time sequence radar data and optical data, and comprises the following steps:
step 1, satellite radar image data of a VV + VH dual-polarization mode of the small reservoir are obtained, and the obtained satellite radar image data are preprocessed to obtain a time sequence backscattering coefficient corresponding to the small reservoir satellite radar image data;
step 2, obtaining DEM data of the small reservoir, and removing data corresponding to the mountain shadow part from the backscattering coefficient obtained in the step 1 based on the DEM data;
establishing a water body sample ROI from backscattering coefficients after mountain shadow removal, counting backscattering coefficients corresponding to the water body sample ROI, and then determining an optimal land and water segmentation threshold value based on a backscattering coefficient statistical result corresponding to the water body sample ROI;
performing water-land segmentation on the image data of the satellite radar of the small reservoir obtained in the step (1) based on the optimal water-land segmentation threshold value to obtain crude extraction result data of the small reservoir;
and 3, acquiring satellite optical image data of the small reservoir, calculating based on the satellite optical image data of the small reservoir to obtain a normalized water body index NDWI, and correcting the crude extraction result data of the small reservoir obtained in the step 2 based on the normalized water body index NDWI to obtain fine extraction result data of the small reservoir.
In a further step 1, the preprocessing sequentially comprises orbit correction processing, thermal noise removal processing, radiometric calibration processing, multiview processing, speckle filtering processing, geocoding processing and decibel processing.
In a further step 2, the backscattering coefficient corresponding to the water body sample ROI is counted, and the statistical average value is taken as an optimal land and water segmentation threshold value.
In a further step 3, normalization difference processing is carried out on the optical image data of the small reservoir satellite by adopting a set waveband, so that the normalized water body index NDWI is obtained.
In a further step 3, in order to distinguish the water body from the non-water body, the maximum value of NDWI is selected from the time sequence normalized water body indexes NDWI, the corresponding part of the area which is more than or equal to the maximum value of NDWI is selected from the crude extraction result data of the small reservoir, the rest is displayed as the background, so that the correction is completed, the fine extraction result data of the small reservoir is obtained, only the small reservoir is displayed in the backward scattering coefficient diagram, and the spatial distribution of the small reservoir is obtained.
The method comprises the steps of extracting and identifying information for a special water body, namely the small reservoir, firstly preprocessing ground distance image GRD data of a VV + VH dual-polarization mode of Sentinel1 by utilizing time sequence Sentinel first data to obtain a decibel backscatter coefficient diagram, and then overlapping DEM to remove the influence of mountain shadow for reducing false identification caused by mountain influence to obtain a time sequence backscatter information diagram corresponding to the small reservoir.
Establishing a water body sample ROI for the preprocessed time sequence decibel backscatter coefficient image data, checking the backscatter value of the water body sample ROI, counting, taking the average value of the backscatter value as an optimal water Liu Fenge threshold value, segmenting the satellite radar image data of the small reservoir, and obtaining the crude extraction result data of the small reservoir.
Then, selecting Sentinel No. two of the same region and time sequence of the small reservoir, namely Sentinel2 optical image data, calculating an NDWI value of each period through band operation, selecting an NDWI maximum value in the time sequence, removing an NDWI negative value region in the small reservoir crude extraction result data, and correcting the small reservoir crude extraction result data to obtain small reservoir fine extraction result data.
The method comprises the steps of establishing a Water body sample ROI by using a preprocessed Sentinel1 data namely a backscattering coefficient decibel graph, determining an optimal land and Water segmentation threshold value based on a backscattering coefficient statistical graph of a sample area, carrying out rough extraction through the land and Water segmentation threshold value, correcting a rough extraction result by utilizing a Normalized Difference Water Index (NDWI) to realize fine extraction, and inverting more reliable and accurate fine Water body distribution information to obtain accurate pond and dam information. Compared with the traditional threshold segmentation method, the influence of water-like ground objects can be effectively removed by using the optical image NDWI to correct the SAR water body coarse body.
Drawings
FIG. 1 is a block flow diagram of the method of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
As shown in fig. 1, the present embodiment is described by taking the satellite data of a sentinel as an example, and specifically includes the following steps:
step 1, image mode selection and decibel processing
Firstly, satellite radar image data of the small reservoir collected by the sentinel I (sentinel-1) is obtained, and due to the fact that the image water characteristics of different polarization modes are different, researches have shown that the image of the cross polarization mode is more suitable for extracting water body information than the image of the same polarization mode, so that the ground distance image GRD data of the VV + VH dual polarization mode of the sentinel I (sentinel-1) is selected for extracting the time sequence backscatter coefficient information of the small reservoir.
Sequentially carrying out orbit correction processing, thermal noise removal processing, radiometric calibration processing, multi-view processing, coherent speckle filtering processing, geocoding processing and decibel processing on satellite radar image data of the small reservoir to obtain backscattering information, wherein the backscattering information is obtained
Track correction processing: the exact track file is downloaded and the track file in the metadata is automatically replaced.
Thermal noise removal processing: since SAR images are all noisy, which affects the accuracy of Radar backscatter signals, the Noise is removed here by the radio → Radiometric → S-1Thermal Noise operation of SNAP software.
Radiometric calibration processing: the Sentinel1 image is typically not radiation corrected and there is significant radiation variation and is therefore achieved by the radio → Radiometric → calibre operation.
Multi-view processing: the method comprises the steps of dividing the length of the whole effective synthetic aperture into 5 sections, imaging the same scene respectively, summing and superposing the obtained images to obtain an SAR image, improving the signal-to-noise ratio of the SAR image, inhibiting speckle noise, improving image interpretability and realizing the method through Radar → Multilookup.
And (3) coherent speckle filtering treatment: because coherent electromagnetic waves are transmitted by the synthetic aperture radar, when echoes of continuous radar pulses are subjected to coherent processing, due to the rough surface, coherent superposition of the electromagnetic waves reflected by each scatterer and the inconsistent distance between each basic scatterer and the sensor, the echoes are incoherent on the phase, so that the intensity of the echoes changes pixel by pixel, and the echoes are represented as particles in a mode, so that black and white spots randomly distributed appear in an SAR image. This is achieved by radius → speed filtration → Single Product speed Filter (where the Filter is selected for referred Lee).
And (3) geocoding processing: in the synthetic aperture Radar side-view imaging, the fluctuation of the Terrain causes great Geometric distortion to SAR images and causes phenomena of perspective shrinkage, overlaying, shading and the like, terrain Correction is required, and the geocoding processing is completed while the Terrain Correction is performed on the images by using a Radar → Geometric → Terrarain Correction → Range-Doppler Terrarain Correction tool.
Decibel processing: the values obtained after the processing are usually small positive values, and the radar backscattering coefficient range is approximately common Gaussian distribution by using decibel processing and is realized by the ratio → Data Conversion → Conversion bases to/from dB.
Step 2, determining a segmentation threshold value and removing mountain shadow
And (3) acquiring DEM data of the small reservoir, and removing data corresponding to the mountain shadow part from the backscattering coefficient obtained in the step (1) based on the DEM data.
Establishing a water body sample ROI from the backscattering coefficient after the mountain shadow is removed, counting the backscattering coefficient corresponding to the water body sample ROI, and then determining an optimal land and water segmentation threshold value based on the backscattering coefficient statistical result corresponding to the water body sample ROI.
And (3) carrying out water-land segmentation on the image data of the satellite radar of the small reservoir obtained in the step (1) based on the optimal water-land segmentation threshold value to obtain crude extraction result data of the small reservoir.
Step 3, finely extracting the information of the small reservoir
And (2) calculating a Normalized Difference Water Index (NDWI) based on the Sentinel-2 optical image data in the Sentinel2 optical image of the Sentinel of the small reservoir, and correcting the crude extraction result data of the small reservoir in the step (2) by using the Normalized Difference Water Index (NDWI) to obtain the fine extraction result data of the small reservoir.
The normalized water body index NDWI is normalized difference value processing carried out by using a Green wave band (Green) and a near infrared wave band (NIR) of a remote sensing image so as to highlight the water body information in the image.
When the optical image data is used for calculating the normalized water body index NDWI, a Sentinel-1 dual-polarized data SDWI water body extraction index formula can be referred to:
SDWI＝ln(10×b1×b2)-8 (1)
in the formula (1), b1 and b2 represent VV and VH bands of Sentiniel 1, respectively
Then remote sensing image processing software ENVI 5.3 is adopted, and a Band math tool is utilized in ENVI 5.3 to realize index calculation as shown in the following formula:
NDWI＝(b1-b2)/(b1+b2) (2)
in the formula (2), b1 and b2 respectively represent Green and NIR bands of Sentiniel 2
And selecting the maximum value of the NDWI from the normalized water body index NDWI, and carrying out fine correction on the coarse extraction result of the small reservoir, wherein the large reservoir is the small reservoir with the retrodispersion coefficient decibel value larger than or equal to the maximum value of the NDWI, and the rest of the large reservoir are displayed as backgrounds, and finally the fine extraction result data of the small reservoir is obtained.
The examples described herein are only for the purpose of describing the preferred embodiments of the present invention, and are not intended to limit the spirit and scope of the present invention, and various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the design concept of the present invention shall fall within the protection scope of the present invention, and the technical contents of the present invention as claimed shall be fully described in the claims.
Claims (5)
1. A method for rapidly identifying a small reservoir based on radar data and optical data cooperation is characterized in that time sequence radar data and optical data are utilized to rapidly identify a special water body, namely the small reservoir, and comprises the following steps:
step 1, satellite radar image data of a VV + VH dual-polarization mode of the small reservoir are obtained, and the obtained satellite radar image data are preprocessed to obtain a time sequence backscattering coefficient corresponding to the small reservoir satellite radar image data;
step 2, obtaining DEM data of the small reservoir, and removing data corresponding to the mountain shadow part from the backscattering coefficient obtained in the step 1 based on the DEM data;
establishing a water body sample ROI from backscattering coefficients after mountain shadow removal, counting backscattering coefficients corresponding to the water body sample ROI, and then determining an optimal land and water segmentation threshold value based on a backscattering coefficient statistical result corresponding to the water body sample ROI;
performing water-land segmentation on the image data of the satellite radar of the small reservoir obtained in the step (1) based on the optimal water-land segmentation threshold value to obtain crude extraction result data of the small reservoir;
and 3, acquiring satellite optical image data of the small reservoir, calculating based on the satellite optical image data of the small reservoir to obtain a normalized water body index NDWI, and correcting the crude extraction result data of the small reservoir obtained in the step 2 based on the normalized water body index NDWI to obtain fine extraction result data of the small reservoir.
2. The method for rapidly identifying the small reservoir based on the cooperation of the radar data and the optical data as claimed in claim 1, wherein the preprocessing comprises track correction processing, thermal noise removal processing, radiometric calibration processing, multi-view processing, speckle filtering processing, geocoding processing and decibel processing in sequence in the step 1.
3. The method for rapidly identifying the small reservoir based on the cooperation of the radar data and the optical data as claimed in claim 1, wherein in the step 2, the backscattering coefficient corresponding to the water body sample ROI is counted, and the statistical average value is taken as an optimal water and land segmentation threshold value.
4. The method for rapidly identifying the small reservoir based on the cooperation of the radar data and the optical data as claimed in claim 1, wherein in the step 3, the normalized difference processing is performed on the optical image data of the small reservoir satellite by using a set waveband, so as to obtain the normalized water body index NDWI.
5. The method as claimed in claim 1, wherein in step 3, the maximum value of NDWI is selected from normalized water body indexes NDWI, the area corresponding to the maximum value of NDWI is selected from the crude extraction result data of the small reservoir, and the rest is displayed as a background, thereby completing the correction to obtain the fine extraction result data of the small reservoir.
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| CN109977801A (en) * | 2019-03-08 | 2019-07-05 | 中国水利水电科学研究院 | A kind of quick Dynamic Extraction method and system of region water body of optical joint and radar |
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