CN109613513B - Optical remote sensing potential landslide automatic identification method considering InSAR deformation factor - Google Patents

Optical remote sensing potential landslide automatic identification method considering InSAR deformation factor Download PDF

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CN109613513B
CN109613513B CN201811590750.3A CN201811590750A CN109613513B CN 109613513 B CN109613513 B CN 109613513B CN 201811590750 A CN201811590750 A CN 201811590750A CN 109613513 B CN109613513 B CN 109613513B
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赵超英
荀张媛
康亚
杨成生
朱武
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Abstract

The invention belongs to the field of potential landslide identification, and discloses an optical remote sensing potential landslide automatic identification method considering InSAR deformation factors, which comprises the following steps: acquiring a deformation rate image and optical remote sensing image data of a target area, and preprocessing the optical remote sensing image to obtain an optical remote sensing image of the target area; calculating to obtain topographic information data of the target area; according to the deformation rate graph of the target area, a plurality of objects are obtained through segmentation, and ground feature classification samples are selected to obtain classification characteristic elements and threshold values of various ground feature classification samples; classifying and eliminating to obtain a target area potential landslide area obtained by using the optical remote sensing image; calculating to obtain a potential landslide object, combining the potential landslide object with the obtained potential landslide area of the target area, and finally obtaining a complete potential landslide area of the target area; the method combines optical remote sensing and InSAR deformation rate information, can quickly and effectively extract potential landslide information, has high automation degree and reliability, and provides technical support for landslide disaster prevention.

Description

Optical remote sensing potential landslide automatic identification method considering InSAR deformation factor
Technical Field
The invention belongs to the field of potential landslide identification, and particularly relates to an optical remote sensing potential landslide automatic identification method considering InSAR deformation factors.
Background
Landslide is a common natural disaster caused by natural factors or human activities, and mainly occurs in mountain areas and valley areas; the landslide is identified in the research area by adopting an optical remote sensing interpretation technology, although the coverage area is large, and the landslide can be identified through human-computer interaction; however, the conventional method for identifying landslide by using optical remote sensing images can only provide semi-quantitative landslide identification results when landslide investigation is carried out, and has the phenomena of missed judgment and wrong judgment, and most of the landslide identification methods are used for positioning the area after landslide occurs, so that early identification of potential landslide areas is difficult to realize, and a lot of landslides are difficult to early warn and effectively prevent and treat in time. Because the SAR image not only contains intensity information, but also contains phase information, centimeter-level or even millimeter-level ground surface deformation in a research area can be obtained through InSAR deformation rate information, and therefore reliability and accuracy of landslide identification and monitoring in the research area are improved; however, the InSAR deformation rate information can only obtain one-dimensional deformation, and phenomena of shadows, inversion of top and bottom, perspective shortening and the like exist, and phenomena of missed judgment and wrong judgment on landslide also exist, and particularly, manual drawing is needed for directly identifying landslide based on the InSAR deformation rate information, so that subjectivity is achieved, and the degree of automation is low.
Disclosure of Invention
The invention aims to solve the problems that the method for identifying landslide by using optical remote sensing images in the prior art can only provide semi-quantitative landslide identification results, the phenomena of judgment omission and judgment errors exist, and the problems of strong subjectivity and low automation degree when the InSAR deformation rate information is used for identifying landslide.
In order to achieve the purpose, the technical scheme is as follows:
an optical remote sensing potential landslide automatic identification method considering InSAR deformation factors specifically comprises the following steps:
step 1, processing an SAR image acquired by an SAR satellite in a research area to obtain a deformation rate map of the research area; selecting a suspected landslide area covering one of the study areas as a target area, and cutting to obtain a deformation rate graph of the target area;
step 2, obtaining optical remote sensing image data of a target area and DEM data of the target area, wherein the optical remote sensing image data comprises panchromatic waveband data and 4 waveband multispectral data; performing orthorectification on the optical remote sensing image data according to the acquired DEM data of the target area to obtain an orthorectification optical remote sensing image comprising panchromatic waveband data and 4 waveband multispectral data; processing the orthoscopic optical remote sensing image to obtain an optical remote sensing image map of a target area; the DEM is an exponential elevation model;
step 3, defining projection and resampling of DEM data of the target area to obtain the resampled DEM data; cutting the resampled DEM data to obtain complete DEM data of a target area, and calculating according to the complete DEM data of the target area to obtain a slope map, a mountain shadow map, a surface relief map and a ground elevation map of the target area;
it is characterized by also comprising:
step 4, defining projection and resampling of the deformation rate graph of the target area to obtain a resampled deformation rate graph; adopting a multi-scale segmentation method to segment the deformation rate in the deformation rate image after resampling and the 4-waveband multispectral data in the optical remote sensing image map of the target area integrally to obtain a plurality of objects;
calculating to obtain attribute values of a plurality of objects according to 4-waveband multispectral data in a slope map, a mountain shadow map, a surface relief map, a ground elevation map and an optical remote sensing image map of a target area;
selecting surface feature classification samples by taking the obtained multiple objects as basic units, and calculating classification feature elements of various surface feature classification samples and threshold values corresponding to the classification feature elements according to the selected surface feature classification samples and the obtained attribute values of the multiple objects;
step 5, classifying the optical remote sensing image map of the target area according to the classification characteristic elements of the classification samples of various ground objects and the threshold values corresponding to the classification characteristic elements to obtain the classification results of various ground objects; obtaining a potential landslide area of a target area obtained by using the optical remote sensing image according to the classification result of various ground objects;
step 6, extracting and obtaining a potential landslide object with missed points according to a deformation rate diagram of the target area and classification results of various ground objects; and merging the missed potential landslide object with the potential landslide area of the target area obtained by using the optical remote sensing image to obtain the potential landslide area of the target area fusing the optical remote sensing image and InSAR deformation rate information.
Further, the step 4 specifically includes the following steps:
step 41, defining projection on the deformation rate map of the target area by adopting a map projection method to obtain the deformation rate map with projection information, and enabling the deformation rate map with the projection information to be consistent with a projection coordinate system of an optical remote sensing image map of the target area; resampling the deformation rate image with the projection information by adopting a cubic convolution method to obtain a resampled deformation rate image, and keeping the resolution ratio of the resampled deformation rate image consistent with that of the optical remote sensing image in the target area;
step 42, taking the deformation rate in the deformation rate image after resampling as a waveband data, and adopting a multi-scale segmentation method to segment the deformation rate in the deformation rate image after resampling and the whole 4-waveband multispectral data in the optical remote sensing image of the target area to obtain a plurality of objects, wherein the objects refer to a pixel set with homogeneity;
step 43, obtaining the slope value, the ground elevation value, the mountain shadow value, the terrain relief value and the 4-waveband multispectral value of all pixels forming the object in each object from the slope map, the mountain shadow map, the surface relief degree, the ground elevation map and the 4-waveband multispectral data in the optical remote sensing image map of the target area respectively, and calculating the average value of the slope value, the average value of the ground elevation value, the average value of the mountain shadow value, the average value of the terrain relief value and the average value of the 4-waveband multispectral value of all pixels forming the object in each object respectively; respectively calculating the NDVI value and the NDWI value of each object through formulas (4) and (5);
taking the average value of slope values, the average value of ground elevation values, the average value of mountain shadow values, the average value of terrain relief values, the average value of 4-waveband multi-spectral values and the NDVI value and the NDWI value of each object of all pixels forming each object as the attribute values of each object to obtain the attribute values of a plurality of objects;
Figure BDA0001920192080000031
Figure BDA0001920192080000041
in the formula, NDVIiIndicating the normalized vegetation index, NDWI, of the ith objectiA normalized water body index representing an ith subject; rhoi(NIR)、ρi(RED)、ρi(GREEN) respectively represents the reflectivity values of the near infrared band, the red band and the GREEN band in the 4-band multispectral data of the ith object, and the value ranges are all [0, 1%]I represents the ith object, i is a natural number greater than 0;
step 44, selecting surface feature classification samples by taking the obtained multiple objects as basic units, wherein the surface feature classification samples comprise water bodies, vegetation, bedrocks, bare land, artificial surfaces, sediments and shadow samples; and calculating the selected ground feature classification samples and the obtained attribute values of the plurality of objects by adopting a classification regression tree algorithm to obtain classification characteristic elements of the various ground feature classification samples and threshold values corresponding to the classification characteristic elements.
Further, the step 5 specifically includes:
the classification characteristic elements of various ground objects and the threshold values corresponding to the classification characteristic elements form a classification rule, and the optical remote sensing image map of the target area is classified by using the classification rule to obtain the classification results of various ground objects; and eliminating the classification result of various ground objects in the plurality of objects to obtain the potential landslide area of the target area obtained by using the optical remote sensing image.
Further, the step 6 specifically includes the following steps:
step 61, superposing the deformation rate image of the target area and the classification results of various ground objects to obtain the deformation rate of each pixel in the optical remote sensing image of the target area; calculating the average value of the deformation rates of all pixels forming the object, and taking the obtained average value as the deformation rate of the object to obtain the deformation rates of a plurality of objects;
step 62, setting two thresholds m and n of the deformation rate, wherein m is less than 0, n is greater than 0, and extracting an object with the deformation rate k within the range of k being less than or equal to m or k being more than or equal to n in the classification results of various ground objects by using a threshold classification method to serve as a potential landslide object with missing classification;
and 63, merging the missed potential landslide object and the potential landslide area of the target area obtained by using the optical remote sensing image through parallel operation to obtain the potential landslide area of the target area fusing the optical remote sensing image and InSAR deformation rate information.
Further, the step 1 specifically comprises the following steps:
processing an SAR image acquired by an SAR satellite in a coverage research area by adopting a coherent point target analysis method in InSAR to obtain a deformation rate diagram of the research area; and selecting a suspected landslide area covering one of the study areas as a target area, and cutting the deformation rate graph of the study area along the boundary of the target area to obtain the deformation rate graph of the target area.
Further, the step 2 specifically includes the following steps:
step 21, acquiring optical remote sensing image data covering a target area through a remote sensing satellite, wherein the optical remote sensing image data comprises panchromatic waveband data with the resolution of 0.61m and 4-waveband multispectral data with the resolution of 2.44m, and the 4 wavebands comprise red, green, blue and near-infrared wavebands;
step 22, acquiring DEM data of a target area through a space shuttle radar terrain mapping mission system; obtaining a complete RPC model by using an RPC file and an RPC model shape carried by optical remote sensing image data; respectively performing orthorectification on panchromatic band data and 4-band multispectral data by utilizing DEM data and a complete RPC model to obtain an orthoscopic optical remote sensing image comprising the panchromatic band data and the 4-band multispectral data, wherein the RPC refers to rational polynomial coefficients;
step 23, fusing the ortho-optical remote sensing image comprising panchromatic band data and 4-band multispectral data by adopting an NNDiffuse Pan imaging algorithm to obtain a fused optical remote sensing image;
step 24, vectorizing the target area range to obtain a target area vector boundary diagram; and cutting the fused optical remote sensing image along the boundary of the vector boundary image of the target area to obtain the optical remote sensing image of the target area.
Further, the step 3 specifically includes the following steps:
step 31, defining projection on the DEM data by adopting a map projection method to obtain the DEM data with projection information, and enabling the DEM data with the projection information to be consistent with a projection coordinate system of an optical remote sensing image map of a target area; resampling the DEM data with the projection information by adopting a triple convolution method to obtain resampled DEM data, and keeping the resolution ratio of the resampled DEM data consistent with that of an optical remote sensing image map of a target area;
step 32, cutting the resampled DEM data along the boundary of the vector boundary diagram of the target area to obtain complete DEM data of the target area; calculating to obtain a gradient map of the target area by adopting a formula (1) according to the complete DEM data of the target area; obtaining a mountain shadow map by adopting a formula (2); obtaining a surface relief degree graph by adopting a formula (3); the ground elevation value is a DEM value, and a ground elevation chart is obtained;
Figure BDA0001920192080000061
wherein slope is slope, fxElevation change rate in X direction, f, in the complete DEM data of the target areayThe elevation change rate in the Y direction in the complete DEM data of the target area is obtained;
Figure BDA0001920192080000062
wherein hillshade is mountain shadow, zenithradIs the radian number, slope, of the solar zenith angle in the optical remote sensing image data of the target arearadFor the complete gradient arc degree, azimuth, of the DEM data of the target arearadIs the radian number, aspect, of the solar ray direction in the optical remote sensing image data of the target arearadThe number of the slope arc in the complete DEM data of the target area is obtained;
R＝Hmax-Hmin，R＞0，Hmax，Hmin∈R (3)
wherein R is topographic relief degree, HmaxFor the maximum elevation, H, within a fixed analysis window in the complete DEM data for the target areaminAnd the lowest elevation in the fixed analysis window in the complete DEM data of the target area.
Compared with the prior art, the invention has the beneficial effects that:
1. the method comprises the steps of adopting a multi-scale segmentation method to segment deformation rate in a deformation rate image after resampling and 4-waveband multispectral data in an optical remote sensing image map of a target area, finally obtaining a potential landslide area of the target area obtained by using the optical remote sensing image, merging a missing potential landslide object obtained according to the deformation rate image of the target area and the potential landslide area of the target area obtained by using the optical remote sensing image, and finally obtaining a complete potential landslide area of the target area; the method combines the optical remote sensing technology and InSAR deformation rate information, can quickly and effectively identify and extract the potential landslide information, has high automation degree and reliability, particularly positions the non-landslide area, and has important significance for monitoring and early warning of the potential landslide.
2. Compared with the traditional landslide identification method by optical remote sensing, the method has the advantages that the identified potential landslide area is more complete, and the phenomena of missing judgment and wrong judgment are reduced; compared with the method that the landslide boundary map is drawn through visual interpretation by using single InSAR deformation rate information, the method has the advantages of more accurate result, high automation degree, no need of field investigation, lower requirement on geological professional knowledge of practitioners, particular suitability for landslide identification research difficult for personnel to reach and provision of technical support for timely landslide disaster prevention.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a potential landslide boundary diagram obtained by a landslide identification method using InSAR deformation rate information;
FIG. 3 is a potential landslide boundary map extracted by a conventional method for identifying landslide using optical remote sensing images;
fig. 4 is a potential landslide boundary map extracted using the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
As shown in fig. 1, the invention provides an optical remote sensing potential landslide automatic identification method considering InSAR deformation factors, which specifically comprises the following steps:
step 1, processing an SAR image acquired by an SAR satellite in a research area to obtain a deformation rate map of the research area; selecting a suspected landslide area covering one of the study areas as a target area, and cutting to obtain a deformation rate graph of the target area;
step 2, obtaining optical remote sensing image data of a target area and DEM data of the target area, wherein the optical remote sensing image data comprises panchromatic waveband data and 4 waveband multispectral data; performing orthorectification on the optical remote sensing image data according to the acquired DEM data of the target area to obtain an orthorectification optical remote sensing image comprising panchromatic waveband data and 4 waveband multispectral data; processing the orthoscopic optical remote sensing image to obtain an optical remote sensing image map of a target area, wherein the optical remote sensing image map of the target area comprises panchromatic waveband data and 4 waveband multispectral data; the DEM is an exponential elevation model;
step 3, defining projection and resampling of DEM data of the target area to obtain the resampled DEM data; cutting the resampled DEM data to obtain complete DEM data of a target area, and calculating according to the complete DEM data of the target area to obtain a slope map, a mountain shadow map, a surface relief map and a ground elevation map of the target area;
step 4, defining projection and resampling of the deformation rate graph of the target area to obtain a resampled deformation rate graph; taking the deformation rate in the deformation rate graph after resampling as band data, and adopting a multi-scale segmentation method to integrally segment the deformation rate in the deformation rate graph after resampling and 4-band multispectral data in an optical remote sensing image graph of a target area to obtain a plurality of objects; calculating to obtain attribute values of a plurality of objects according to 4-waveband multispectral data in a slope map, a mountain shadow map, a surface relief map, a ground elevation map and an optical remote sensing image map of a target area; selecting surface feature classification samples by taking the obtained multiple objects as basic units, and calculating classification feature elements of various surface feature classification samples and threshold values corresponding to the classification feature elements according to the selected surface feature classification samples and the obtained attribute values of the multiple objects;
step 5, classifying the optical remote sensing image map of the target area according to the classification characteristic elements of the classification samples of various ground objects and the threshold values corresponding to the classification characteristic elements to obtain the classification results of various ground objects; obtaining a potential landslide area of a target area obtained by using the optical remote sensing image according to the classification result of various ground objects;
step 6, extracting and obtaining a potential landslide object with missed points according to a deformation rate diagram of the target area and classification results of various ground objects; and merging the missed potential landslide object with the potential landslide area of the target area obtained by using the optical remote sensing image to obtain the potential landslide area of the target area fusing the optical remote sensing image and InSAR deformation rate information.
The method comprises the steps of adopting a multi-scale segmentation method to segment deformation rate in a deformation rate image after resampling and 4-waveband multispectral data in an optical remote sensing image map of a target area, finally obtaining a potential landslide area of the target area obtained by using the optical remote sensing image, merging a missing potential landslide object obtained according to the deformation rate image of the target area and the potential landslide area of the target area obtained by using the optical remote sensing image, and finally obtaining a complete potential landslide area of the target area; the method combines the optical remote sensing technology and InSAR deformation rate information, can quickly and effectively identify and extract the potential landslide information, has strong automation and high reliability, particularly positions the non-landslide area, and has important significance for monitoring and early warning of the potential landslide.
Specifically, the step 1 specifically comprises the following steps:
processing an SAR image acquired by an SAR satellite in a coverage research area by adopting an IPTA method in InSAR to obtain a deformation rate diagram of the research area; selecting a suspected landslide area covering one of the study areas as a target area, and cutting a deformation rate graph of the study area along the boundary of the target area to obtain a deformation rate graph of the target area;
the InSAR represents a synthetic aperture radar interferometric technique; IPTA is all called Interferometric PointTarget Analysis and represents coherent point target Analysis.
According to the method, the deformation rate graph of the research area is cut, and the deformation rate graph and the deformation rate of the target area are obtained.
Specifically, the step 2 specifically includes the following steps:
step 21, acquiring optical remote sensing image data covering a target area through a remote sensing satellite, wherein the optical remote sensing image data comprises panchromatic waveband data with the resolution of 0.61m and 4-waveband multispectral data with the resolution of 2.44m, and the 4 wavebands comprise red, green, blue and near-infrared wavebands;
step 22, acquiring DEM data of the target area through an SRTM system; forming and obtaining a complete RPC model according to the RPC file and the RPC model carried by the optical remote sensing image data; respectively carrying out orthorectification on panchromatic data and 4-waveband multispectral data by utilizing DEM data and a complete RPC model to obtain an orthoscopic optical remote sensing image comprising the panchromatic data and the 4-waveband multispectral data, wherein the 4 wavebands comprise red, green, blue and near-infrared wavebands;
the SRTM represents a space shuttle radar terrain mapping mission; the DEM represents a digital elevation model; the RPC is called all the rational Polynomial coeffients and represents rational Polynomial Coefficients;
step 23, fusing the ortho-optical remote sensing image comprising panchromatic band data and 4-band multispectral data by adopting NNDiffuse (near Neighbor diffusion) Pan Sharpening algorithm to obtain a fused optical remote sensing image;
step 24, vectorizing the target area range to obtain a target area vector boundary diagram; and cutting the fused optical remote sensing image along the boundary of the vector boundary image of the target area to obtain an optical remote sensing image map of the target area, wherein the optical remote sensing image map of the target area comprises panchromatic waveband data and 4 waveband multispectral data.
In the method, projection difference caused by terrain is corrected by performing orthorectification on panchromatic data and 4-waveband multispectral data; by fusing the panchromatic image and the 4-waveband multispectral image in the orthometric optical remote sensing image, the image environment and the interpretation reliability of target identification are improved, the aim of improving the spatial resolution of the optical remote sensing image while keeping multispectral waveband information is fulfilled, and the optical remote sensing image of a target area is finally obtained.
Specifically, the step 3 specifically includes the following steps:
step 31, defining projection on the DEM data by adopting a map projection method to obtain the DEM data with projection information, and enabling the DEM data with the projection information to be consistent with a projection coordinate system of an optical remote sensing image map of a target area; resampling the DEM data with the projection information by adopting a triple convolution method to obtain resampled DEM data, and keeping the resolution ratio of the resampled DEM data consistent with that of an optical remote sensing image map of a target area;
step 32, cutting the resampled DEM data along the boundary of the vector boundary diagram of the target area to obtain complete DEM data of the target area; calculating to obtain a gradient map of the target area by adopting a formula (1) according to the complete DEM data of the target area; obtaining a mountain shadow map by adopting a formula (2); obtaining a surface relief degree graph by adopting a formula (3); the ground elevation value is a DEM value, and a ground elevation chart is obtained; the terrain information comprises gradient, elevation, mountain shadow and terrain relief;
Figure BDA0001920192080000101
wherein slope is slope, fxElevation change rate in X direction, f, in the complete DEM data of the target areayElevation changes in Y direction in DEM data for the integrity of the target areaRate;
Figure BDA0001920192080000102
wherein hillshade is mountain shadow, zenithradIs the radian number, slope, of the solar zenith angle in the optical remote sensing image data of the target arearadFor the complete gradient arc degree, azimuth, of the DEM data of the target arearadIs the radian number, aspect, of the solar ray direction in the optical remote sensing image data of the target arearadThe number of the slope arc in the complete DEM data of the target area is obtained;
R＝Hmax-Hmin，R＞0，Hmax，Hmin∈R (3)
wherein R is topographic relief degree, HmaxFor the maximum elevation, H, within a fixed analysis window in the complete DEM data for the target areaminAnd the lowest elevation in the fixed analysis window in the complete DEM data of the target area.
According to the method, complete DEM data of a target area are obtained by processing and cutting DEM data, and a slope map, a mountain shadow map, a relief map and a ground elevation map of the target area are obtained.
Specifically, the step 4 specifically includes the following steps:
step 41, defining projection on the deformation rate map of the target area by adopting a map projection method to obtain the deformation rate map with projection information, and enabling the deformation rate map with the projection information to be consistent with a projection coordinate system of an optical remote sensing image map of the target area; resampling the deformation rate image with the projection information by adopting a cubic convolution method to obtain a resampled deformation rate image, and keeping the resolution ratio of the resampled deformation rate image consistent with that of the optical remote sensing image in the target area;
step 42, taking the deformation rate in the deformation rate image after resampling as a waveband data, and adopting a multi-scale segmentation method to segment the deformation rate in the deformation rate image after resampling and the whole 4-waveband multispectral data in the optical remote sensing image of the target area to obtain a plurality of objects, wherein the objects refer to a pixel set with homogeneity;
step 43, obtaining the slope value, the ground elevation value, the mountain shadow value, the terrain relief value and the 4-waveband multispectral value of all pixels forming the object in each object from the slope map, the mountain shadow map, the surface relief degree, the ground elevation map and the 4-waveband multispectral data in the optical remote sensing image map of the target area respectively, and calculating the average value of the slope value, the average value of the ground elevation value, the average value of the mountain shadow value, the average value of the terrain relief value and the average value of the 4-waveband multispectral value of all pixels forming the object in each object respectively; respectively calculating the NDVI value and the NDWI value of each object through formulas (4) and (5);
taking the average value of slope values, the average value of ground elevation values, the average value of mountain shadow values, the average value of terrain relief values, the average value of 4-waveband multi-spectral values and the NDVI value and the NDWI value of each object of all pixels forming each object as the attribute values of each object to obtain the attribute values of a plurality of objects;
Figure BDA0001920192080000121
Figure BDA0001920192080000122
in the formula, NDVIiIndicating the normalized vegetation index, NDWI, of the ith objectiA normalized water body index representing an ith subject; rhoi(NIR)、ρi(RED)、ρi(GREEN) respectively represents the reflectivity values of the near infrared band, the red band and the GREEN band in the 4-band multispectral data of the ith object, and the value ranges are all [0, 1%]I represents the ith object, i is a natural number greater than 0;
step 44, selecting surface feature classification samples by taking the obtained multiple objects as basic units, wherein the surface feature classification samples comprise water bodies, vegetation, bedrocks, bare land, artificial surfaces, sediments and shadow samples, and calculating the selected surface feature classification samples and the obtained attribute values of the multiple objects by adopting a CART algorithm to obtain classification characteristic elements of various surface feature classification samples and thresholds corresponding to the classification characteristic elements;
the CART is all called Classification and Regression Tree and represents a Classification Regression Tree;
the method is used for participating in segmentation through the deformation rate graph of the target area, compared with DEM data in the prior art, the boundary of the ground objects can be clearly and clearly outlined, ground object classification samples are selected, the calculated amount is reduced, and subsequent classification is facilitated.
Specifically, the step 5 specifically includes:
the classification characteristic elements of various ground objects and the threshold values corresponding to the classification characteristic elements form a classification rule, and the optical remote sensing image map of the target area is classified by using the classification rule to obtain the classification results of various ground objects; and eliminating the classification result of various ground objects in the plurality of objects to obtain the potential landslide area of the target area obtained by using the optical remote sensing image.
According to the method, the potential landslide area of the target area obtained by the optical remote sensing image is obtained according to the classification characteristic elements of various ground feature classification samples and the threshold values corresponding to the classification characteristic elements, and the complete landslide area can be obtained by combining and identifying subsequent InSAR deformation data.
Specifically, the step 6 specifically includes the following steps:
step 61, superposing the deformation rate image of the target area and the classification results of various ground objects to obtain the deformation rate of each pixel in the optical remote sensing image of the target area; calculating the average value of the deformation rates of all pixels forming the object, and taking the obtained average value as the deformation rate of the object to obtain the deformation rates of a plurality of objects;
step 62, setting two thresholds m and n of the deformation rate, wherein m is less than 0, n is greater than 0, and extracting an object with the deformation rate k within the range of k being less than or equal to m or k being more than or equal to n in the classification results of various ground objects by using a threshold classification method to serve as a potential landslide object with missing classification;
and 63, merging the missed potential landslide object and the potential landslide area of the target area obtained by using the optical remote sensing image through parallel operation to obtain the potential landslide area of the target area fusing the optical remote sensing image and InSAR deformation rate information.
The method integrates the optical remote sensing image and InSAR deformation data to identify the potential landslide, compared with the traditional method for identifying the landslide by using the optical remote sensing image, the identified potential landslide area is more complete, and the phenomena of missing judgment and wrong judgment are reduced; compared with the method that the landslide boundary map is drawn through visual interpretation by single InSAR deformation data, the method has the advantages that the result is more in accordance with the terrain trend, the accuracy is higher, the automation degree is high, field investigation is not needed, the requirement on geological professional knowledge of practitioners is lower, the method is particularly suitable for landslide recognition research difficult to reach by the personnel, and technical support is provided for timely preventing landslide disasters.
Examples
The method collects ALOS/PALSAR rail lifting data covering the Udongde reservoir area of the Jinshajiang river basin and Quickbird-02 data covering the potential landslide area of the double Longtan. The acquired ALOS/PALSAR orbit ascending data has 20 scenes in total, and the image date range is from 2007 to 2011 and 3 months; the acquired Quickbird-02 data has 1 scene, comprises panchromatic data with the resolution of 0.61m and multispectral data with the resolution of 2.44m, the acquisition date is 11 months and 15 days in 2009, the solar altitude angle is 43.6 degrees, the solar azimuth angle is 162.7 degrees, and the cloud cover is 0.00 percent.
Procedure of experiment
Step 1, processing the acquired SAR image of the Wudongde reservoir area to obtain a deformation rate map of the Wudongde reservoir area, and identifying 22 suspected landslide areas in the Wudongde reservoir area; and selecting one suspected landslide area as a target area, and acquiring a deformation rate map of the target area.
Step 2, obtaining optical remote sensing image data of a target area and DEM data of the target area, wherein the optical remote sensing image data comprises panchromatic waveband data and 4 waveband multispectral data; performing orthorectification on the optical remote sensing image data according to the acquired DEM data of the target area to obtain an orthorectification optical remote sensing image comprising panchromatic waveband data and 4 waveband multispectral data; processing the orthoscopic optical remote sensing image to obtain an optical remote sensing image map of a target area;
step 3, defining projection and resampling of DEM data of the target area to obtain the resampled DEM data; cutting the resampled DEM data to obtain complete DEM data of a target area, and calculating by adopting a formula (1) according to the complete DEM data of the target area to obtain a gradient map of the target area; obtaining a mountain shadow map by adopting a formula (2); obtaining a surface relief degree graph by adopting a formula (3); the ground elevation value is a DEM value, and a ground elevation chart is obtained;
Figure BDA0001920192080000141
wherein slope is slope, fxElevation change rate in X direction, f, in the complete DEM data of the target areayThe elevation change rate in the Y direction in the complete DEM data of the target area is obtained;
Figure BDA0001920192080000142
wherein hillshade is mountain shadow, zenithradIs the radian number, slope, of the solar zenith angle in the optical remote sensing image data of the target arearadFor the complete gradient arc degree, azimuth, of the DEM data of the target arearadIs the radian number, aspect, of the solar ray direction in the optical remote sensing image data of the target arearadThe number of the slope arc in the complete DEM data of the target area is obtained;
R＝Hmax-Hmin，R＞0，Hmax，Hmin∈R (3)
wherein R is topographic relief degree, HmaxFor the maximum elevation, H, within a fixed analysis window in the complete DEM data for the target areaminAnd the lowest elevation in the fixed analysis window in the complete DEM data of the target area.
Step 4, defining projection and resampling of the deformation rate graph of the target area to obtain a resampled deformation rate graph; taking the deformation rate in the deformation rate graph after resampling as band data, and adopting a multi-scale segmentation method to integrally segment the deformation rate in the deformation rate graph after resampling and 4-band multispectral data in an optical remote sensing image graph of a target area to obtain a plurality of objects;
respectively obtaining the slope value, the ground elevation value, the mountain shadow value, the terrain relief value and the 4-waveband multispectral value of all pixels forming the object in each object from 4-waveband multispectral data in a slope map, a mountain shadow map, a surface relief degree, a ground elevation map and an optical remote sensing image map of the target area, and respectively calculating the average value of the slope values, the average value of the ground elevation values, the average value of the mountain shadow values, the average value of the terrain relief values and the average value of the 4-waveband multispectral values of all pixels forming the object in each object; respectively calculating the NDVI value and the NDWI value of each object through formulas (4) and (5);
taking the average value of slope values, the average value of ground elevation values, the average value of mountain shadow values, the average value of terrain relief values, the average value of 4-waveband multi-spectral values and the NDVI value and the NDWI value of each object of all pixels forming each object as the attribute values of each object to obtain the attribute values of a plurality of objects;
Figure BDA0001920192080000151
Figure BDA0001920192080000152
in the formula, NDVIiIndicating the normalized vegetation index, NDWI, of the ith objectiA normalized water body index representing an ith subject; rhoi(NIR)、ρi(RED)、ρi(GREEN) respectively represents the reflectivity values of the near infrared band, the red band and the GREEN band in the 4-band multispectral data of the ith object, and the value ranges are all [0, 1%]I represents the ith object, i is a natural number greater than 0;
selecting surface feature classification samples by taking an object as a basic unit, and calculating the selected surface feature classification samples and attribute values thereof by adopting a classification regression tree algorithm to obtain classification feature elements of various surface feature classification samples and thresholds corresponding to the classification feature elements;
step 5, classifying the optical remote sensing image map of the target area according to the classification characteristic elements of the classification samples of various ground objects and the threshold values corresponding to the classification characteristic elements to obtain the classification results of various ground objects; obtaining a potential landslide area of a target area obtained by using the optical remote sensing image according to the classification result of various ground objects;
step 6, extracting and obtaining a potential landslide object with missed points according to a deformation rate diagram of the target area and classification results of various ground objects; and merging the missed potential landslide object with the potential landslide area of the target area obtained by using the optical remote sensing image to obtain the potential landslide area of the target area which is drawn by using a black line and fused with the optical remote sensing image and InSAR deformation rate information in the figure 4.
Obtaining a potential landslide boundary diagram drawn by black lines in the figure 2 by utilizing an InSAR deformation rate information landslide identification method; compared with a landslide identification method by utilizing InSAR deformation rate information, the method provided by the invention has the advantages that the obtained potential landslide area of the target area fusing the optical remote sensing image and the InSAR deformation rate information is more in accordance with terrain trend, more accurate and high in automation degree.
Extracting a potential landslide boundary graph drawn by black lines in the graph 3 by using a traditional landslide identification method through an optical remote sensing image; compared with the traditional method for identifying landslide by using the optical remote sensing image, the method extracts the potential landslide object which is missed in the process of identifying landslide by using the traditional optical remote sensing image, obtains the potential landslide area of the target area which is fused with the optical remote sensing image and InSAR deformation rate information more completely, and reduces the phenomena of missed judgment and wrong judgment.
The above disclosure is only for the specific embodiment of the present invention, but the embodiment of the present invention is not limited thereto, and any variations that can be made by those skilled in the art should fall within the scope of the present invention.

Claims (6)

1. An optical remote sensing potential landslide automatic identification method considering InSAR deformation factors specifically comprises the following steps:
step 1, processing an SAR image acquired by an SAR satellite in a research area to obtain a deformation rate map of the research area; selecting a suspected landslide area covering one of the study areas as a target area, and cutting to obtain a deformation rate graph of the target area;
step 2, obtaining optical remote sensing image data of a target area and DEM data of the target area, wherein the optical remote sensing image data comprises panchromatic waveband data and 4 waveband multispectral data; performing orthorectification on the optical remote sensing image data according to the acquired DEM data of the target area to obtain an orthorectification optical remote sensing image comprising panchromatic waveband data and 4 waveband multispectral data; processing the orthoscopic optical remote sensing image to obtain an optical remote sensing image map of a target area; the DEM is an exponential elevation model;
step 3, defining projection and resampling of DEM data of the target area to obtain the resampled DEM data; cutting the resampled DEM data to obtain complete DEM data of a target area, and calculating according to the complete DEM data of the target area to obtain a slope map, a mountain shadow map, a surface relief map and a ground elevation map of the target area;
it is characterized by also comprising:
step 4, defining projection and resampling of the deformation rate graph of the target area to obtain a resampled deformation rate graph; adopting a multi-scale segmentation method to segment the deformation rate in the deformation rate image after resampling and the 4-waveband multispectral data in the optical remote sensing image map of the target area integrally to obtain a plurality of objects;
calculating to obtain attribute values of a plurality of objects according to 4-waveband multispectral data in a slope map, a mountain shadow map, a surface relief map, a ground elevation map and an optical remote sensing image map of a target area;
selecting surface feature classification samples by taking the obtained multiple objects as basic units, and calculating classification feature elements of various surface feature classification samples and threshold values corresponding to the classification feature elements according to the selected surface feature classification samples and the obtained attribute values of the multiple objects;
the step 4 specifically comprises the following steps:
step 41, defining projection on the deformation rate map of the target area by adopting a map projection method to obtain the deformation rate map with projection information, and enabling the deformation rate map with the projection information to be consistent with a projection coordinate system of an optical remote sensing image map of the target area; resampling the deformation rate image with the projection information by adopting a cubic convolution method to obtain a resampled deformation rate image, and keeping the resolution ratio of the resampled deformation rate image consistent with that of the optical remote sensing image in the target area;
step 42, taking the deformation rate in the deformation rate image after resampling as a waveband data, and adopting a multi-scale segmentation method to segment the deformation rate in the deformation rate image after resampling and the whole 4-waveband multispectral data in the optical remote sensing image of the target area to obtain a plurality of objects, wherein the objects refer to a pixel set with homogeneity;
step 43, obtaining the slope value, the ground elevation value, the mountain shadow value, the terrain relief value and the 4-waveband multispectral value of all pixels forming the object in each object from the slope map, the mountain shadow map, the surface relief degree, the ground elevation map and the 4-waveband multispectral data in the optical remote sensing image map of the target area respectively, and calculating the average value of the slope value, the average value of the ground elevation value, the average value of the mountain shadow value, the average value of the terrain relief value and the average value of the 4-waveband multispectral value of all pixels forming the object in each object respectively; respectively calculating the NDVI value and the NDWI value of each object through formulas (4) and (5);
taking the average value of slope values, the average value of ground elevation values, the average value of mountain shadow values, the average value of terrain relief values, the average value of 4-waveband multi-spectral values and the NDVI value and the NDWI value of each object of all pixels forming each object as the attribute values of each object to obtain the attribute values of a plurality of objects;
Figure FDA0002440399950000021
Figure FDA0002440399950000022
in the formula, NDVIiIndicating the normalized vegetation index, NDWI, of the ith objectiA normalized water body index representing an ith subject; rhoi(NIR)、ρi(RED)、ρi(GREEN) respectively represents the reflectivity values of the near infrared band, the red band and the GREEN band in the 4-band multispectral data of the ith object, and the value ranges are all [0, 1%]I represents the ith object, i is a natural number greater than 0;
step 44, selecting surface feature classification samples by taking the obtained multiple objects as basic units, wherein the surface feature classification samples comprise water bodies, vegetation, bedrocks, bare land, artificial surfaces, sediments and shadow samples; calculating the selected ground feature classification samples and the obtained attribute values of the plurality of objects by adopting a classification regression tree algorithm to obtain classification characteristic elements of the various ground feature classification samples and threshold values corresponding to the classification characteristic elements;
step 5, classifying the optical remote sensing image map of the target area according to the classification characteristic elements of the classification samples of various ground objects and the threshold values corresponding to the classification characteristic elements to obtain the classification results of various ground objects; obtaining a potential landslide area of a target area obtained by using the optical remote sensing image according to the classification result of various ground objects;
step 6, extracting and obtaining a potential landslide object with missed points according to a deformation rate diagram of the target area and classification results of various ground objects; and merging the missed potential landslide object with the potential landslide area of the target area obtained by using the optical remote sensing image to obtain the potential landslide area of the target area fusing the optical remote sensing image and InSAR deformation rate information.
2. The method for automatically identifying the optical remote sensing potential landslide considering the InSAR deformation factor as claimed in claim 1, wherein the step 5 specifically comprises:
the classification characteristic elements of various ground objects and the threshold values corresponding to the classification characteristic elements form a classification rule, and the optical remote sensing image map of the target area is classified by using the classification rule to obtain the classification results of various ground objects; and eliminating the classification result of various ground objects in the plurality of objects to obtain the potential landslide area of the target area obtained by using the optical remote sensing image.
3. The method for automatically identifying the InSAR deformation factor-considered optical remote sensing potential landslide as claimed in claim 1, wherein the step 6 specifically comprises the following steps:
step 61, superposing the deformation rate image of the target area and the classification results of various ground objects to obtain the deformation rate of each pixel in the optical remote sensing image of the target area; calculating the average value of the deformation rates of all pixels forming the object, and taking the obtained average value as the deformation rate of the object to obtain the deformation rates of a plurality of objects;
step 62, setting two thresholds m and n of the deformation rate, wherein m is less than 0, n is greater than 0, and extracting an object with the deformation rate k within the range of k being less than or equal to m or k being more than or equal to n in the classification results of various ground objects by using a threshold classification method to serve as a potential landslide object with missing classification;
and 63, merging the missed potential landslide object and the potential landslide area of the target area obtained by using the optical remote sensing image through parallel operation to obtain the potential landslide area of the target area fusing the optical remote sensing image and InSAR deformation rate information.
4. The method for automatically identifying the InSAR deformation factor-considered optical remote sensing potential landslide as claimed in claim 1, wherein the step 1 is as follows:
processing an SAR image acquired by an SAR satellite in a coverage research area by adopting a coherent point target analysis method in InSAR to obtain a deformation rate diagram of the research area; and selecting a suspected landslide area covering one of the study areas as a target area, and cutting the deformation rate graph of the study area along the boundary of the target area to obtain the deformation rate graph of the target area.
5. The method for automatically identifying the InSAR deformation factor-considered optical remote sensing potential landslide as claimed in claim 1, wherein the step 2 specifically comprises the following steps:
step 21, acquiring optical remote sensing image data covering a target area through a remote sensing satellite, wherein the optical remote sensing image data comprises panchromatic waveband data with the resolution of 0.61m and 4-waveband multispectral data with the resolution of 2.44m, and the 4 wavebands comprise red, green, blue and near-infrared wavebands;
step 22, acquiring DEM data of a target area through a space shuttle radar terrain mapping mission system; obtaining a complete RPC model by using an RPC file and an RPC model shape carried by optical remote sensing image data; respectively performing orthorectification on panchromatic band data and 4-band multispectral data by utilizing DEM data and a complete RPC model to obtain an orthoscopic optical remote sensing image comprising the panchromatic band data and the 4-band multispectral data, wherein the RPC refers to rational polynomial coefficients;
step 23, fusing the ortho-optical remote sensing image comprising panchromatic band data and 4-band multispectral data by adopting an NNDiffuse Pan imaging algorithm to obtain a fused optical remote sensing image;
step 24, vectorizing the target area range to obtain a target area vector boundary diagram; and cutting the fused optical remote sensing image along the boundary of the vector boundary image of the target area to obtain the optical remote sensing image of the target area.
6. The method for automatically identifying the InSAR deformation factor-considered optical remote sensing potential landslide as claimed in claim 1, wherein the step 3 specifically comprises the following steps:
step 31, defining projection on the DEM data by adopting a map projection method to obtain the DEM data with projection information, and enabling the DEM data with the projection information to be consistent with a projection coordinate system of an optical remote sensing image map of a target area; resampling the DEM data with the projection information by adopting a triple convolution method to obtain resampled DEM data, and keeping the resolution ratio of the resampled DEM data consistent with that of an optical remote sensing image map of a target area;
step 32, cutting the resampled DEM data along the boundary of the vector boundary diagram of the target area to obtain complete DEM data of the target area; calculating to obtain a gradient map of the target area by adopting a formula (1) according to the complete DEM data of the target area; obtaining a mountain shadow map by adopting a formula (2); obtaining a surface relief degree graph by adopting a formula (3); the ground elevation value is a DEM value, and a ground elevation chart is obtained;
Figure FDA0002440399950000051
wherein slope is slope, fxElevation change rate in X direction, f, in the complete DEM data of the target areayThe elevation change rate in the Y direction in the complete DEM data of the target area is obtained;
hillshade＝255×((cos(zenithrad)×cos(sloperad))+(sin(zenithrad)×sin(sloperad)×cos(azimuthrad-aspectrad)))
Figure FDA0002440399950000052
wherein hillshade is mountain shadow, zenithradIs the radian number, slope, of the solar zenith angle in the optical remote sensing image data of the target arearadFor the complete gradient arc degree, azimuth, of the DEM data of the target arearadIs the radian number, aspect, of the solar ray direction in the optical remote sensing image data of the target arearadThe number of the slope arc in the complete DEM data of the target area is obtained;
R＝Hmax-Hmin,R>0,Hmax,Hmin∈R (3)
wherein R is topographic relief degree, HmaxFor the maximum elevation, H, within a fixed analysis window in the complete DEM data for the target areaminIs a target ofThe lowest elevation within the fixed analysis window in the regionally complete DEM data.
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