Deformation slope extraction method, device and equipment based on multi-source data

Abstract

The invention relates to the technical field of geological disasters, and discloses a deformation slope extraction method, device and equipment based on multi-source data, and the method comprises the steps: obtaining a target optical image, three-dimensional topographic data and SAR image data of a target region; superposing the data to obtain a comprehensive layer; color visualization analysis is carried out in the comprehensive layer based on SAR image data, and all potential deformation slopes are determined; for any potential deformation slope, judging whether the potential deformation slope has reasonability or not based on the comprehensive layer; and if so, marking the boundary of the potential deformation slope in the comprehensive layer. According to the method, errors or limitation caused by a single data source are reduced through multiple data sources, the color difference between the deformed slope and the surrounding area is recognized through color visualization analysis, the boundary of the deformed slope is marked more accurately, high-precision extraction of the deformed slope is achieved, and a reference basis is provided for early warning, monitoring and treatment of geological disasters.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster technology, and in particular to a deformation slope extraction method, device and equipment based on multi-source data. Background Art

[0002] Landslide refers to the process of a large amount of rock, soil or debris moving down a slope. The frequent occurrence of landslide disasters will cause huge losses of life and property, and have a serious impact on people's quality of life and the sustainable development of society. In view of this, the research on landslide early warning is particularly important. The stability of the slope has become the focus of landslide early warning research. Therefore, the accurate extraction of deformation slope information has become a key prerequisite for landslide early warning work.

[0003] Currently, deformation slopes can be extracted in many ways, such as extracting deformation slopes based on deformation data, but the factors considered are too single, resulting in incomplete extraction and general accuracy. In addition, deformation slopes can be automatically extracted by relying on algorithms, but the extraction accuracy is low. Summary of the invention

[0004] In view of this, the present invention provides a deformation slope extraction method, device and equipment based on multi-source data to solve the problem of inaccurate extraction in existing deformation slope extraction methods.

[0005] In a first aspect, the present invention provides a deformation slope extraction method based on multi-source data, the method comprising:

[0006] Obtain target optical images, three-dimensional terrain data and SAR image data of the target area;

[0007] The target optical image, three-dimensional terrain data and SAR image data of the target area are superimposed to obtain a comprehensive layer of the target area;

[0008] In the comprehensive layer, color visualization analysis is performed based on SAR image data to determine all potential deformation slopes in the target area. The color of the potential deformation slope in the comprehensive layer is different from that of other areas.

[0009] For any potential deformation slope, based on the comprehensive layer, determine whether the potential deformation slope is reasonable;

[0010] When the potential deformation slope is reasonable, the boundary of the potential deformation slope is marked in the comprehensive layer.

[0011] The deformation slope extraction method based on multi-source data provided by the embodiment of the present invention provides a rich source of information through various types of data. The complementarity between different data sources can reduce the errors or limitations that may be caused by a single data source, thereby improving the reliability and accuracy of the deformation slope extraction result. The multi-source data are superimposed together to form a comprehensive layer containing multiple information, which is helpful to more comprehensively understand the geomorphological characteristics and changes of the target area. Then, color visualization analysis is performed in the comprehensive layer, and the color difference between the deformation slope and the surrounding area can be directly observed, which is convenient for quickly identifying potential deformation slopes. Then, the rationality of the potential deformation slope is judged, and some misjudged or unreasonable areas can be excluded, thereby improving the reliability of deformation slope extraction. Finally, the boundaries of reasonable deformation slopes are accurately marked, and high-precision extraction of deformation slopes is achieved. The marked deformation slope boundaries provide an important reference basis for subsequent geological disaster warning, monitoring and governance.

[0012] In an optional embodiment, color visualization analysis is performed based on SAR image data in a comprehensive layer to determine all potential deformation slopes in the target area, including:

[0013] The InSAR technology is used to process the SAR image data to obtain the deformation value of each grid unit in the comprehensive layer;

[0014] Using the natural discontinuity method, multiple display colors are determined based on the deformation value of each grid unit;

[0015] For each grid cell whose deformation value is within a preset display range, based on the deformation value of the grid cell, a display color and a color value of the grid cell are determined, wherein the color value represents the display color of the grid cell and its depth;

[0016] Based on the color value of the grid cell, a color corresponding to the color value of the grid cell is set for the grid cell in the comprehensive layer;

[0017] For each grid cell, when the difference between the color value of the grid cell and the color value of any connected cell of the grid cell is greater than a preset difference threshold, the grid cell is determined as a target grid cell;

[0018] The connected region algorithm is used to determine the potential deformation slope of the target area based on all target grid cells.

[0019] The deformation slope extraction method based on multi-source data provided by the embodiment of the present invention can accurately measure the surface micro-deformation through the InSAR technology to obtain accurate deformation values, and then use the natural discontinuity method to determine the optimal deformation value interval division scheme according to the deformation value, thereby determining the number of display colors, and intuitively displaying areas with different deformation degrees. Then, for each grid cell whose deformation value is within the preset display range, the display color and color value of the grid cell are determined, which not only reflects the color itself, but also expresses the depth of the color, thereby more accurately describing the degree of deformation. By setting a preset difference threshold, grid cells with significant deformation changes can be quickly screened out as the key analysis objects of potential deformation slopes. All connected target grid cells can be identified and marked through the connected region algorithm to form a complete potential deformation slope area, thereby achieving high-precision extraction of deformation slopes.

[0020] In an optional implementation, a natural discontinuity method is used to determine multiple display colors based on the deformation value of each grid unit, including:

[0021] Dividing the deformation value interval based on the deformation value of each grid unit to obtain multiple division schemes;

[0022] For each partitioning scheme, determine the total variance of the deformation values ​​of all grid cells and the deformation value interval in the partitioning scheme;

[0023] The number of deformation value intervals included in the partitioning scheme corresponding to the minimum total variance is determined as the target number;

[0024] A target number of display colors is determined from the color ramp, the target number of display colors including both cool and warm colors.

[0025] The deformation slope extraction method based on multi-source data provided by an embodiment of the present invention can find the most stable division scheme by comparing the total variance of different division schemes, determine the target number of display colors from the color band to better distinguish deformation values ​​of different categories, and use a combination of cool and warm tones to make the extraction of the deformation slope more intuitive and clear, making it more intuitive.

[0026] In an optional implementation, a connected region algorithm is used to determine the potential deformation slope of the target area based on all target grid cells, including:

[0027] Traverse all target grid cells and add a first mark to the first target grid cell;

[0028] Continue to traverse all target grid cells that have not been marked, and when the next target grid cell is a connected cell of other target grid cells, add the minimum mark among the marks of all connected cells of the next target grid cell to the next target grid cell;

[0029] Alternatively, when the second target grid cell does not belong to a connected cell of any target grid cell, a second mark is added to the second target grid cell, where the second mark is accumulated by adding the first mark;

[0030] Continue the above process of traversing the target grid cells that have not been marked and adding cumulative marks or minimum marks until all target grid cells are marked;

[0031] Correcting the mark of each target grid cell to the minimum mark among the marks of all connected cells of the target grid cell;

[0032] Taking the target grid cells with consistent markings and continuous positions as a deformation region, obtaining at least one deformation region;

[0033] For any deformation region, the deformation region and all deformation regions whose distances to the deformation region are less than a preset distance threshold are merged as potential deformation slopes.

[0034] The deformation slope extraction method based on multi-source data provided by the embodiment of the present invention ensures that each target grid unit is correctly marked through a connected region algorithm, and then corrects it after marking. In addition, by setting a distance threshold, adjacent deformation areas are reasonably merged, so that the extraction of potential deformation slopes is more scientific and reliable.

[0035] In an optional implementation, for any potential deformation slope, judging whether the potential deformation slope is reasonable based on the comprehensive layer includes:

[0036] Determine the texture, optical characteristics and buildings of the potential deformable slope based on the target optical image in the comprehensive layer, and determine the terrain parameters of the potential deformable slope based on the three-dimensional terrain data in the comprehensive layer;

[0037] Based on the texture and optical characteristics of the potential deformation slope, determine whether the potential deformation slope is optically reasonable;

[0038] Based on the buildings with potential deformation slopes, determine whether the potential deformation slopes have architectural rationality;

[0039] Based on the topographic parameters of the potential deformation slope, determine whether the potential deformation slope has topographic rationality;

[0040] Under the condition that the potential deformation slope is optically reasonable, architecturally reasonable and topographically reasonable, it is determined that the potential deformation slope is reasonable;

[0041] Alternatively, when the potential deformation slope does not satisfy any one of optical rationality, architectural rationality and terrain rationality, it is determined that the potential deformation slope is not rational.

[0042] The deformable slope extraction method based on multi-source data provided in an embodiment of the present invention provides a complete description of potential deformable slopes by collecting data in multiple dimensions, and then performs a comprehensive evaluation in three dimensions of optics, architecture and terrain based on the above information, providing a comprehensive rationality evaluation and enhancing the credibility of the extraction results.

[0043] In an optional implementation, obtaining a target optical image, three-dimensional terrain data, and SAR image data of a target area includes:

[0044] Acquire multiple historical optical images of the target area during a preset research period;

[0045] Determine a target optical image that meets the target research requirements from multiple historical optical images;

[0046] Based on the time corresponding to the target optical image, three-dimensional terrain data of the target area is obtained;

[0047] Obtain the SAR database of the target area;

[0048] Based on the surface characteristics of the target area, the preset study period and the availability of SAR image data, SAR image data are obtained from the SAR database.

[0049] The deformation slope extraction method based on multi-source data provided by the embodiment of the present invention improves the accuracy and credibility of the analysis results by screening out the target optical images that meet the research requirements, then obtains the corresponding three-dimensional terrain data based on the time corresponding to the target optical image, performs temporal and spatial alignment to facilitate comprehensive analysis, and then accurately selects suitable SAR image data according to the surface characteristics of the target area and the research period. By obtaining multiple types of data, the integrity and diversity of the data are ensured, the accuracy and reliability of deformation slope extraction are improved, and the deviation caused by a single data source is reduced.

[0050] In an optional implementation, when the potential deformation slope is reasonable, after marking the boundary of the potential deformation slope in the comprehensive layer, the method further includes:

[0051] Based on the target optical image and three-dimensional terrain data in the comprehensive layer, the impact area of ​​the potential deformable slope is determined. The impact area represents the area affected when a landslide disaster occurs on the potential deformable slope.

[0052] The impact areas of potential deformable slopes are marked in the comprehensive layer.

[0053] The deformable slope extraction method based on multi-source data provided by the embodiment of the present invention can more accurately predict the impact area when a landslide occurs on a potential deformable slope through comprehensive analysis of multi-source data, provide a scientific basis for subsequent risk assessment, and improve the scientificity and reliability of landslide disaster warning.

[0054] In a second aspect, the present invention provides a deformation slope extraction device based on multi-source data, the device comprising:

[0055] An acquisition module is used to acquire target optical images, three-dimensional terrain data and SAR image data of the target area;

[0056] The superposition module is used to superimpose the target optical image, three-dimensional terrain data and SAR image data of the target area to obtain a comprehensive layer of the target area;

[0057] The first determination module is used to perform color visualization analysis based on SAR image data in the comprehensive layer to determine all potential deformation slopes in the target area, and the color of the potential deformation slope in the comprehensive layer is different from that of other areas;

[0058] A judgment module is used to judge whether the potential deformation slope is reasonable based on the comprehensive layer for any potential deformation slope;

[0059] The extraction module is used to mark the boundaries of potential deformation slopes in the comprehensive layer when the potential deformation slopes are reasonable.

[0060] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the deformation slope extraction method based on multi-source data of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0061] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the deformation slope extraction method based on multi-source data of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 is a flow chart of a deformation slope extraction method based on multi-source data according to an embodiment of the present invention;

[0064] Figure 2 is a schematic diagram of a potential deformation slope according to an embodiment of the present invention;

[0065] Figure 3 is a schematic diagram of another potential deformation slope according to an embodiment of the present invention;

[0066] Figure 4 is a structural block diagram of a deformation slope extraction device based on multi-source data according to an embodiment of the present invention;

[0067] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0069] The related technology extracts deformation slopes based on deformation data, but its considerations are too single, resulting in incomplete extraction and general accuracy. It can also rely on algorithms to automatically extract deformation slopes, but its extraction accuracy is low. The deformation slope extraction method based on multi-source data provided by the embodiment of the present invention provides a rich source of information through various types of data. The complementarity between different data sources can reduce the errors or limitations that may be caused by a single data source, thereby improving the reliability and accuracy of the deformation slope extraction results, and performing color visualization analysis, which can directly observe the color difference between the deformation slope and the surrounding area, facilitate rapid identification of potential deformation slopes, and then judge the rationality of the potential deformation slopes. Finally, the boundaries of reasonable deformation slopes are more accurately marked, achieving high-precision extraction of deformation slopes. The marked deformation slope boundaries provide an important reference for subsequent geological disaster warning, monitoring and governance.

[0070] According to an embodiment of the present invention, an embodiment of a deformation slope extraction method based on multi-source data is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0071] In this embodiment, a deformation slope extraction method based on multi-source data is provided, which can be used in terminals such as computers, etc. Figure 1 is a flow chart of a deformation slope extraction method based on multi-source data according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0072] Step S101, obtain the target optical image, three-dimensional terrain data and SAR image data of the target area. Specifically, the target area can be accurately delineated through the Google Earth platform using the search function or polygon drawing tool. Then, with the help of the "historical image" function of the Google Earth platform, the target optical image and three-dimensional terrain data of the target area are provided. Among them, the three-dimensional terrain data, such as a digital elevation model, can intuitively display the terrain undulations and landform features. The target optical image can clearly display surface features such as surface vegetation, water bodies, and buildings. The SAR (Synthetic Aperture Radar) image data is a microwave remote sensing image that reflects surface features through phase difference and intensity changes, and the data source can be selected to achieve data acquisition. Since the relevant technology only considers a single data when extracting the deformation slope, resulting in low extraction accuracy, the embodiment of the present invention obtains multi-source data for deformation slope extraction to improve the extraction accuracy.

[0073] Step S102, superimpose the target optical image, three-dimensional terrain data and SAR image data of the target area to obtain a comprehensive layer of the target area. Specifically, the above three types of data are imported into the GIS (Geographic Information System) platform, and the layer superposition is achieved by adjusting the transparency of different data to obtain a comprehensive layer of the target area, which can intuitively display various surface features of the target area and provide a basis for the subsequent extraction of deformation slopes.

[0074] Step S103, color visualization analysis is performed based on the SAR image data in the comprehensive layer to determine all potential deformation slopes in the target area. The color of the potential deformation slope in the comprehensive layer is different from that of other areas. Specifically, the deformation value of each grid cell in the comprehensive layer can be obtained by using the phase information of the SAR image data, so that different colors are set for each grid cell based on the deformation value. Color visualization facilitates rapid identification and extraction of deformation slopes.

[0075] Step S104: for any potential deformation slope, based on the comprehensive layer, determine whether the potential deformation slope is reasonable. Specifically, since the comprehensive layer is composed of multi-source data, the authenticity of the potential deformation slope identified by color is evaluated based on the various surface features it displays, to ensure that the extracted potential deformation slope is reasonable and reduce misjudgment.

[0076] Step S105: If the potential deformation slope is reasonable, the boundary of the potential deformation slope is marked in the comprehensive layer. Specifically, in the comprehensive layer of the GIS platform, vector tools are used, such as white lines, to accurately locate and draw the reasonable boundary of the potential deformation slope. By marking, the information of the potential deformation slope can be intuitively displayed, and the deformation slope can be accurately extracted, which provides important decision-making basis for disaster warning and emergency response.

[0077] The deformation slope extraction method based on multi-source data provided by the embodiment of the present invention provides a rich source of information through various types of data. The complementarity between different data sources can reduce the errors or limitations that may be caused by a single data source, thereby improving the reliability and accuracy of the deformation slope extraction result. The multi-source data are superimposed together to form a comprehensive layer containing multiple information, which is helpful to more comprehensively understand the geomorphological characteristics and changes of the target area. Then, color visualization analysis is performed in the comprehensive layer, and the color difference between the deformation slope and the surrounding area can be directly observed, which is convenient for quickly identifying potential deformation slopes. Then, the rationality of the potential deformation slope is judged, and some misjudged or unreasonable areas can be excluded, thereby improving the reliability of deformation slope extraction. Finally, the boundaries of reasonable deformation slopes are accurately marked, and high-precision extraction of deformation slopes is achieved. The marked deformation slope boundaries provide an important reference basis for subsequent geological disaster warning, monitoring and governance.

[0078] In this embodiment, a deformation slope extraction method based on multi-source data is provided, which can be used for the above-mentioned terminal. The method specifically includes the following steps:

[0079] Step S201, obtaining a target optical image, three-dimensional terrain data and SAR image data of a target area.

[0080] Specifically, the above step S201 includes:

[0081] Step S2011, obtain multiple historical optical images of the target area in a preset research period. Specifically, the historical optical images show the surface characteristics of the target area at different times. Assuming that the researchers extract the deformation slope on December 10, the preset research period should be as close to this date as possible to ensure the timeliness of the research. For example, it can be 2 days, that is, all historical optical images of December 8 and December 9 are obtained for subsequent analysis.

[0082] Step S2012, determining a target optical image that meets the target research requirements from multiple historical optical images. Specifically, the target research requirements may be that the surface features are most obvious, the interference factors are minimal, and the amount of information is maximum. For example, the vegetation has a smaller impact so that other surface features can be better identified; the snow and cloud cover in the image are as small as possible so as not to affect the visibility of surface details; the lighting conditions are good to ensure the clarity and contrast of the image. Optionally, the target research requirements may be set by the researchers themselves, and the embodiments of the present invention do not limit this. Assuming that the target research requirements are the above three conditions, determine a target optical image that meets the target research requirements from multiple historical optical images.

[0083] Step S2013, based on the time corresponding to the target optical image, obtain the three-dimensional terrain data of the target area. Specifically, assuming that the target optical image is taken at 15:00 on December 10, the three-dimensional terrain data of the target area at the time closest to 15:00 on December 10 is obtained to achieve alignment in time and space.

[0084] Step S2014: Acquire a SAR database of the target area. Specifically, the SAR database may be obtained from a satellite platform, or from a related enterprise or individual, which is not limited in the embodiment of the present invention.

[0085] Step S2015, based on the surface characteristics of the target area, the preset research period and the availability of SAR image data, SAR image data is obtained from the SAR database. Specifically, by comprehensively considering the surface characteristics of the target area, the preset research period and the availability of data, the surface characteristics of the target area are analyzed to determine suitable data, and according to the requirements of the preset research period, the data with complete coverage is selected, and the quality and applicability of the data are evaluated to ensure that it meets the research requirements, and finally the SAR image data is obtained.

[0086] Step S202: superimpose the target optical image, three-dimensional terrain data and SAR image data of the target area to obtain a comprehensive layer of the target area. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0087] Step S203, performing color visualization analysis based on the SAR image data in the comprehensive layer to determine all potential deformation slopes in the target area, where the color of the potential deformation slope in the comprehensive layer is different from that of other areas.

[0088] Specifically, the above step S203 includes:

[0089] Step S2031, the SAR image data is processed by the InSAR technology to obtain the deformation value of each grid unit in the comprehensive layer. Specifically, since the InSAR technology is divided into multiple types, and each InSAR technology has different technical advantages. For example, for the differential InSAR (Differential-InSAR, D-InSAR) technology, the advantage of this technology lies in its simple and intuitive data processing process, but it relies on highly repeatable SAR image data, which is suitable for monitoring short-term surface changes, and has relatively low sensitivity, and is not suitable for large-scale deformation monitoring; for the small baseline subset (Small Baseline Subset-InSAR, SBAS-InSAR) technology, it can obtain higher monitoring accuracy and is suitable for medium-range surface deformation monitoring, but this technology requires a large amount of SAR image data, and the data processing process is relatively complicated; for the permanent scatterer InSAR (Persistent Scatterer-InSAR, PS-InSAR) technology, this technology can obtain high-precision and high-sensitivity monitoring results, which is suitable for monitoring complex terrain and nonlinear deformation, but its data processing is also complex, and requires a large amount of SAR image data, and requires the surface scatterer to have high stability. According to the research objectives of the researchers and the accuracy and resolution requirements of deformation monitoring, an InSAR processing technology suitable for the data acquired by the embodiment of the present invention is selected from a variety of InSAR processing technologies to obtain the deformation value of each grid unit in the comprehensive layer.

[0090] Step S2032, using the natural discontinuity method, based on the deformation value of each grid unit, determines a plurality of display colors.

[0091] In some optional implementations, the above step S2032 includes:

[0092] Step a1, divide the deformation value interval based on the deformation value of each grid cell to obtain multiple division schemes. Specifically, by dividing the deformation value interval according to the deformation value of the grid cell, multiple different division schemes can be obtained. For example, the first division scheme: [-0.3, -0.2), [-0.2, -0.1), [-0.1, 0), [0, 0.1), [0.1, 0.2) and [0.2, 0.3]; the second division scheme: [-0.3, -0.1), [-0.1, 0.1) and [0.1, 0.3]; the third division scheme: [-0.3, 0) and [0, 0.3]. Optionally, the above division schemes are only examples and are not limited. The deformation value intervals in each division scheme will be different. The selection of these intervals should try to cover the deformation values ​​of all grid cells and ensure that the width of each interval is reasonable.

[0093] Step a2, for each partitioning scheme, determine the total variance between the deformation values ​​of all grid cells and the deformation value interval in the partitioning scheme. Specifically, assuming that there are three partitioning schemes, for any partitioning scheme, calculate the total variance between the deformation values ​​of all grid cells and all deformation value intervals in the partitioning scheme. The smaller the total variance, the smaller the data fluctuation in the interval, and the better the partitioning effect.

[0094] Step a3: determine the number of deformation value intervals included in the partitioning scheme corresponding to the minimum total variance as the target number. Specifically, compare the total variances of different partitioning schemes, and select the partitioning scheme with the minimum total variance. The number of deformation value intervals in the scheme is the target number.

[0095] Step a4, determining a target number of display colors from the color band, the target number of display colors including both cold colors and warm colors. Specifically, the target number of display colors are selected from the predefined color band. Usually, the display colors include both cold colors and warm colors, and the cold colors are used to represent grid cells with negative deformation values ​​to characterize the sinking or collapse of the surface, and the warm colors are used to represent grid cells with positive deformation values ​​to characterize the rise or uplift of the surface, so as to intuitively distinguish the surface conditions of different grid cells.

[0096] Step S2033, for each grid cell whose deformation value is within the preset display range, the display color and color value of the grid cell are determined based on the deformation value of the grid cell, and the color value represents the display color of the grid cell and its depth. Specifically, the preset display range is the deformation value range, and only the grid cells whose deformation values ​​are within the range are displayed in color, and the grid cells that are not within the range do not display color or display default colors, etc. Assume that the target number obtained in the above steps is 2, that is, two display colors are selected from the color band: green (cold color) and brown (warm color). For any two grid cells, the deformation values ​​are -0.15 and -0.3 respectively, and both should be set to green. The smaller the negative deformation value, the darker the color, and the greater the surface subsidence or collapse. Therefore, the grid cell with a deformation value of -0.3 should be darker in green than the grid cell with a deformation value of -0.15. Similarly, for any two other grid cells, the deformation values ​​are 0.2 and 0.25 respectively, and both should be set to brown. The larger the positive deformation value, the darker the color, and the greater the surface rise or uplift. Therefore, the grid cell with a deformation value of 0.25 should be darker brown than the grid cell with a deformation value of 0.2. The color value of each grid cell is its corresponding RGB (RedGreenBlue, red, green and blue color model) value, which is used to represent the color of the grid cell and its depth.

[0097] Step S2034, based on the color value of the grid cell, a color corresponding to the color value of the grid cell is set for the grid cell in the comprehensive layer. Specifically, according to the color value of each grid cell obtained in step S2032, a corresponding color is set in the comprehensive layer so that the deformation of each position in the target area can be intuitively displayed.

[0098] Step S2035, for each grid cell, when the difference between the color value of the grid cell and the color value of any connected cell of the grid cell is greater than a preset difference threshold, the grid cell is determined as a target grid cell. Specifically, the connectivity definition includes 4-connectivity and 8-connectivity. 4-connectivity means that a grid cell is connected to four adjacent grid cells above, below, left and right, and 8-connectivity means that a grid cell is connected to eight adjacent grid cells around it. The embodiment of the present invention takes 4-connectivity as an example for explanation. Other connectivity modes can also be used in actual use, and the embodiment of the present invention does not limit this. Since different color values ​​represent different display colors and the depth of colors, the size and positive and negative of the deformation value are represented in this way. If the color value of a certain area is significantly higher than that of the surrounding area, it means that its deformation value is significantly higher than that of the surrounding area, and it is more likely to be a deformation slope. Therefore, for any grid cell, it has 4 connected cells, and the difference between its color value and each connected cell is determined respectively, and the one with a difference greater than the preset difference threshold is determined as the target grid cell as the key focus unit for extracting the deformation slope.

[0099] Step S2036: using a connected region algorithm, determine the potential deformation slope of the target area based on all target grid cells.

[0100] In some optional implementations, the above step S2036 includes:

[0101] Step b1, traverse all target grid cells and add a first mark to the first target grid cell. Specifically, assuming that the marks of all grid cells corresponding to the target area are as shown in the following formula (1), add a first mark "1" to the first target grid cell. Optionally, a default mark "0" can be added to the non-target grid cells, and the target grid cells that have not been traversed are not marked temporarily.

[0102]

[0103] Step b2, continue to traverse all unmarked target grid cells, and if the next target grid cell belongs to a connected cell of other target grid cells, add the minimum mark among the marks of all connected cells of the next target grid cell to the next target grid cell. Specifically, continue to traverse the unmarked target grid cells, and if the next target grid cell belongs to a connected cell of multiple target grid cells, add the minimum mark among the marks of all connected cells of the next target grid cell to the next target grid cell.

[0104] Alternatively, in step b3, when the second target grid cell does not belong to the connected cell of any target grid cell, a second mark is added to the second target grid cell, and the second mark is obtained by accumulating the first mark. Specifically, judging the target grid cell in the first row and sixth column in the above formula (1), it can be known that it does not belong to the connected cell of any target grid cell, so the second mark is accumulated on the basis of the first mark, and the second mark "2" is added to it, and the following formula (2) is obtained.

[0105]

[0106] Step b4, continue the above process of traversing the target grid cells that have not been marked and adding cumulative marks or minimum marks until all target grid cells are marked. Specifically, continue traversing until all target grid cells are marked, and obtain the following formula (3). Assume that when marking the target grid cell located in the second row and first column of the above formula (2), since its right side is its connected unit, but it has not been marked at this time, the connected unit on its right side is not considered, and the target grid cell in the second row and first column is accumulated on the basis of the mark of the first row and sixth column, and the mark "3" is added. When marking the target grid cell located in the second row and second column of the above formula (2), since both its left and right sides are its connected units, but its right side has not been marked at this time, the connected unit on its right side is not considered, and the mark of its connected unit is added to the target grid cell in the second row and second column, that is, the mark "3". When marking the target grid cell located in the second row and third column in the above formula (2), since its upper and left sides are both its connected cells and both have marks, "1" and "3" respectively, the smallest mark of the marks of its two connected cells, that is, mark "1", is added to the target grid cell in the second row and third column.

[0107]

[0108] Step b5, correct the mark of each target grid cell to the minimum mark among the marks of all connected cells of the target grid cell. Specifically, since there are cases where the connected cells of the target grid cell are not marked during the first traversal, such as the target grid cells in the second row and first column and the second row and second column as exemplified in step b4, it is necessary to correct the mark of each target grid cell to make the connected area analysis more accurate. The mark of each target grid cell is corrected by referring to the method of marking the target grid cell in the second row and third column, so that the mark of each target grid cell is minimized, and the final marking result is shown in the following formula (4).

[0109]

[0110] Step b6, taking the target grid cells with consistent markings and continuous positions as a deformation region, and obtaining at least one deformation region. Specifically, taking the target grid cells with consistent markings and continuous positions as a deformation region, two deformation regions with markings "1" and "2" can be obtained from the above formula (4). Optionally, there is no position discontinuity in the above formula (4), that is, independent target grid cells, so they are not considered, and such target grid cells can be discarded in practical applications.

[0111] Step b7, for any deformation region, merge the deformation region and all deformation regions whose distances to the deformation region are less than a preset distance threshold as potential deformation slopes. Specifically, for any deformation region, merge all deformation regions whose distances to the deformation region are less than a preset distance threshold as potential deformation slopes. Through the merging operation, some larger and continuous deformation slope regions can be obtained, which further improves the accuracy of extracting deformation slopes, making the extraction of potential deformation slopes more scientific and reliable.

[0112] Step S204: for any potential deformation slope, based on the comprehensive layer, determine whether the potential deformation slope is reasonable.

[0113] Specifically, the above step S204 includes:

[0114] Step S2041, based on the target optical image in the comprehensive layer, the texture, optical characteristics and buildings of the potential deformable slope are determined, and the terrain parameters of the potential deformable slope are determined based on the three-dimensional terrain data in the comprehensive layer. Specifically, since the comprehensive layer is formed by the superposition of multi-source data, the above information of the potential deformable slope is determined based on the comprehensive layer to provide a data basis for rationality judgment. The texture is the texture characteristics of the slope surface, such as roughness, crack distribution, etc., which helps to understand the physical state of the slope; the optical characteristics include information such as the color and reflectivity of the slope, which helps to understand the changes in the slope surface; the building is a building located on the slope; the terrain parameters include elevation, slope, slope direction, etc., which describe the morphology and geographical environment of the slope and help to evaluate the stability of the slope.

[0115] Step S2042, based on the texture and optical features of the potential deformation slope, determine whether the potential deformation slope is optically reasonable. Specifically, the texture and optical features of the potential deformation slope are compared with the actual deformation slope to determine its optical rationality. For example, if no physical changes of the actual deformation slope are found in the texture, there may be irrationality.

[0116] Step S2043, based on the buildings on the potential deformable slope, determine whether the potential deformable slope has architectural rationality. Specifically, since there cannot be buildings on a deformable slope, if the optical image data shows that there are buildings on the potential deformable slope, the potential deformable slope does not have architectural rationality, indicating that it may not be a real deformable slope.

[0117] Step S2044: Based on the terrain parameters of the potential deformable slope, determine whether the potential deformable slope has terrain rationality. Specifically, determine whether the terrain parameters of the potential deformable slope conform to the geometric characteristics of the actual slope in combination with geomorphological principles to determine its terrain rationality.

[0118] Step S2045: If the potential deformable slope has optical rationality, architectural rationality and topographic rationality, the potential deformable slope is determined to be rational. Specifically, if a potential deformable slope satisfies the above three rationalities at the same time, it is considered to be more likely to be a real deformable slope.

[0119] Alternatively, in step S2046, if the potential deformation slope does not satisfy any one of optical rationality, architectural rationality and terrain rationality, it is determined that the potential deformation slope is not rational. Specifically, if a potential deformation slope does not satisfy any of the above rationalities, it is considered that the possibility of it being a real deformation slope is small.

[0120] Step S205: if the potential deformation slope is reasonable, mark the boundary of the potential deformation slope in the comprehensive layer. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0121] In some optional embodiments, Figure 2 is a schematic diagram of a potential deformation slope according to an embodiment of the present invention, such as Figure 2 As shown, it is assumed that the number of targets obtained by the natural discontinuity method in step S2032 is 2, that is, two display colors are selected from the color band: green and brown, to intuitively display the target area. The potential deformation slope is marked by a white line, and different degrees of green represent different degrees of sinking or collapse. Figure 3 is a schematic diagram of another potential deformation slope according to an embodiment of the present invention, such as Figure 3 As shown, it is assumed that the target number obtained by the natural discontinuity method in step S2032 is 10, that is, the number of colors selected from the color band is as follows: Figure 3 The ten display colors shown on the left are used to intuitively display the target area. Potential deformation slopes are marked with white lines, and different deformation values ​​correspond to different display colors and their color depths. By marking potential deformation slopes in the comprehensive layer, accurate extraction of deformation slopes is achieved, which helps to intuitively display the location of deformation slopes, understand the deformation degree of different locations of deformation slopes, and observe the deformation degree of other areas in the target area, which can provide important reference for subsequent geological disaster warning, monitoring and governance.

[0122] Step S206, based on the target optical image and three-dimensional terrain data in the comprehensive layer, determine the impact area of ​​the potential deformable slope, the impact area represents the area affected when a landslide disaster occurs on the potential deformable slope. Specifically, through the target optical image, analyze the vegetation coverage, soil erosion, and human activities around the reasonable potential deformable slope, and at the same time, with the help of the three-dimensional terrain model, combined with the terrain parameters such as the slope, slope aspect, and elevation of the potential deformable slope and the principles of geomorphology, determine the area that may be affected if a landslide disaster occurs on the potential deformable slope.

[0123] Step S207: Mark the impact area of ​​the potential deformation slope in the comprehensive layer. Specifically, use vector tools in the GIS platform to mark the impact area, providing strong support for geological disaster assessment and subsequent research and prevention needs.

[0124] The deformation slope extraction method based on multi-source data provided by the embodiment of the present invention provides a rich source of information through various types of data. The complementarity between different data sources can reduce the errors or limitations that may be caused by a single data source, thereby improving the reliability and accuracy of the deformation slope extraction result. The multi-source data are superimposed together to form a comprehensive layer containing multiple information, which is helpful to more comprehensively understand the geomorphological characteristics and changes of the target area. Then, color visualization analysis is performed in the comprehensive layer, and the color difference between the deformation slope and the surrounding area can be directly observed, which is convenient for quickly identifying potential deformation slopes. Then, the rationality of the potential deformation slope is judged, and some misjudged or unreasonable areas can be excluded, thereby improving the reliability of deformation slope extraction. Finally, the boundaries of reasonable deformation slopes are accurately marked, and high-precision extraction of deformation slopes is achieved. The marked deformation slope boundaries provide an important reference basis for subsequent geological disaster warning, monitoring and governance.

[0125] In this embodiment, a deformation slope extraction device based on multi-source data is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made are not repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0126] This embodiment provides a deformation slope extraction device based on multi-source data, such as Figure 4 As shown, including:

[0127] The acquisition module 401 is used to acquire the target optical image, three-dimensional terrain data and SAR image data of the target area.

[0128] The superposition module 402 is used to superimpose the target optical image, three-dimensional terrain data and SAR image data of the target area to obtain a comprehensive layer of the target area.

[0129] The first determination module 403 is used to perform color visualization analysis based on the SAR image data in the comprehensive layer to determine all potential deformation slopes in the target area, and the color of the potential deformation slope in the comprehensive layer is different from that of other areas.

[0130] The judgment module 404 is used to judge whether any potential deformation slope is reasonable based on the comprehensive layer map.

[0131] The extraction module 405 is used to mark the boundary of the potential deformation slope in the comprehensive layer when the potential deformation slope is reasonable.

[0132] In some optional implementations, the first determining module 403 includes:

[0133] The data processing unit is used to process the SAR image data using the InSAR technology to obtain the deformation value of each grid unit in the comprehensive layer.

[0134] The first determination unit is used to determine a plurality of display colors based on the deformation value of each grid unit by adopting a natural discontinuity method.

[0135] The second determining unit is used to determine the display color and color value of each grid cell whose deformation value is within the preset display range based on the deformation value of the grid cell, wherein the color value represents the display color of the grid cell and its depth.

[0136] Set cell, which is used to set the color corresponding to the color value of the grid cell in the comprehensive layer for the grid cell based on the color value of the grid cell.

[0137] The third determining unit is configured to determine, for each grid cell, a grid cell as a target grid cell when a difference between a color value of the grid cell and a color value of any connected cell of the grid cell is greater than a preset difference threshold.

[0138] The fourth determining unit is used to determine the potential deformation slope of the target area based on all target grid cells by using a connected region algorithm.

[0139] In some optional implementations, the first determining unit includes:

[0140] The division sub-unit is used to divide the deformation value interval based on the deformation value of each grid unit to obtain multiple division schemes.

[0141] The calculation subunit is used to determine, for each partitioning scheme, the total variance between the deformation values ​​of all grid cells and the deformation value interval in the partitioning scheme.

[0142] The first determining subunit is used to determine the number of deformation value intervals included in the partitioning scheme corresponding to the minimum total variance as the target number.

[0143] The second determination subunit is used to determine a target number of display colors from the color band, where the target number of display colors includes both cool-toned colors and warm-toned colors.

[0144] In some optional implementations, the fourth determining unit includes:

[0145] The traversal subunit is used to traverse all target grid cells and add a first mark to the first target grid cell.

[0146] The first marking subunit is used to continue traversing all unmarked target grid cells, and if the next target grid cell is a connected cell of other target grid cells, add the minimum mark among the marks of all connected cells of the next target grid cell to the next target grid cell.

[0147] Alternatively, the second marking subunit is used to add a second mark to the second target grid cell when the second target grid cell does not belong to a connected cell of any target grid cell, and the second mark is obtained by accumulating the first marks.

[0148] The second marking subunit is used to continue the above process of traversing the target grid cells without adding marks and adding cumulative marks or minimum marks until all target grid cells are marked.

[0149] The correction subunit is used to correct the mark of each target grid cell to the minimum mark among the marks of all connected cells of the target grid cell.

[0150] The third determining subunit is used to take the target grid cells with consistent markings and continuous positions as a deformation region to obtain at least one deformation region.

[0151] The merging subunit is used to merge, for any deformation region, the deformation region and all deformation regions whose distances to the deformation region are less than a preset distance threshold, as potential deformation slopes.

[0152] In some optional implementations, the determination module 404 includes:

[0153] The fifth determination unit is used to determine the texture, optical characteristics and buildings of the potential deformable slope based on the target optical image in the comprehensive layer, and to determine the terrain parameters of the potential deformable slope based on the three-dimensional terrain data in the comprehensive layer.

[0154] The first judging unit is used to judge whether the potential deformation slope is optically reasonable based on the texture and optical characteristics of the potential deformation slope.

[0155] The second judgment unit is used to judge whether the potential deformation slope has architectural rationality based on the building of the potential deformation slope.

[0156] The third judgment unit is used to judge whether the potential deformable slope has terrain rationality based on the terrain parameters of the potential deformable slope.

[0157] The sixth determining unit is used to determine that the potential deformable slope is reasonable when the potential deformable slope is optically reasonable, architecturally reasonable, and topographically reasonable.

[0158] Alternatively, the seventh determination unit is used to determine that the potential deformation slope is not reasonable when the potential deformation slope does not meet any one of optical rationality, architectural rationality and terrain rationality.

[0159] In some optional implementations, the acquisition module 401 includes:

[0160] The first acquisition unit is used to acquire a plurality of historical optical images of the target area during a preset research period.

[0161] The eighth determination unit is used to determine a target optical image that meets the target research requirements from a plurality of historical optical images.

[0162] The second acquisition unit is used to acquire three-dimensional terrain data of the target area based on the time corresponding to the target optical image.

[0163] The third acquisition unit is used to acquire a SAR database of the target area.

[0164] The fourth acquisition unit is used to acquire the SAR image data from the SAR database based on the surface characteristics of the target area, the preset research period and the availability of the SAR image data.

[0165] In some optional implementations, after the extraction module 405, the device further includes:

[0166] The second determination module is used to determine the impact area of ​​the potential deformable slope based on the target optical image and three-dimensional terrain data in the comprehensive layer, and the impact area represents the area affected when the potential deformable slope occurs a landslide disaster.

[0167] The annotation module is used to annotate the impact area of ​​potential deformable slopes in the comprehensive layer.

[0168] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0169] The deformation slope extraction device based on multi-source data in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0170] The embodiment of the present invention also provides a computer device having the above Figure 4 The deformation slope extraction device based on multi-source data is shown.

[0171] See also Figure 5 , Figure 5is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0172] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0173] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0174] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0175] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0176] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0177] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0178] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0179] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0180] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A deformation slope extraction method based on multi-source data, characterized in that: The method comprises: Obtain target optical images, three-dimensional terrain data and SAR image data of the target area; Superimposing the target optical image, three-dimensional terrain data and SAR image data of the target area to obtain a comprehensive layer of the target area; Performing color visualization analysis based on the SAR image data in the comprehensive layer to determine all potential deformation slopes in the target area, wherein the color of the potential deformation slope in the comprehensive layer is different from that of other areas; For any potential deformation slope, judging whether the potential deformation slope is reasonable based on the comprehensive layer; In the case where the potential deformation slope is reasonable, the boundary of the potential deformation slope is marked in the comprehensive layer.

2. The method according to claim 1, characterized in that The color visualization analysis is performed in the comprehensive layer based on the SAR image data to determine all potential deformation slopes in the target area, including: The SAR image data is processed using InSAR technology to obtain a deformation value of each grid unit in the comprehensive layer; Using the natural discontinuity method, multiple display colors are determined based on the deformation value of each grid unit; For each grid cell whose deformation value is within a preset display range, determining the display color and color value of the grid cell based on the deformation value of the grid cell, wherein the color value represents the display color of the grid cell and its depth; Based on the color value of the grid cell, setting a color corresponding to the color value of the grid cell for the grid cell in the comprehensive layer; For each grid cell, when the difference between the color value of the grid cell and the color value of any connected cell of the grid cell is greater than a preset difference threshold, the grid cell is determined as a target grid cell; A connected region algorithm is used to determine the potential deformation slope of the target region based on all target grid cells.

3. The method according to claim 2, characterized in that The natural discontinuity method is used to determine a plurality of display colors based on the deformation value of each grid unit, including: Dividing the deformation value interval based on the deformation value of each grid unit to obtain multiple division schemes; For each partitioning scheme, determining the total variance of the deformation values ​​of all grid cells and the deformation value interval in the partitioning scheme; The number of deformation value intervals included in the partitioning scheme corresponding to the minimum total variance is determined as the target number; A target number of display colors is determined from the color band, wherein the target number of display colors includes both cool-tone colors and warm-tone colors.

4. The method according to claim 2, characterized in that: The method of using a connected region algorithm to determine the potential deformation slope of the target area based on all target grid cells includes: Traverse all target grid cells and add a first mark to the first target grid cell; Continue to traverse all target grid cells that have not been marked, and when the next target grid cell is a connected cell of other target grid cells, add the minimum mark among the marks of all connected cells of the next target grid cell to the next target grid cell; Alternatively, when the second target grid cell does not belong to a connected cell of any target grid cell, a second mark is added to the second target grid cell, where the second mark is accumulated by adding the first marks; Continue the above process of traversing the target grid cells that have not been marked and adding cumulative marks or minimum marks until all target grid cells are marked; Correcting the mark of each target grid cell to the minimum mark among the marks of all connected cells of the target grid cell; Taking the target grid cells with consistent markings and continuous positions as a deformation region, obtaining at least one deformation region; For any deformation region, the deformation region and all deformation regions whose distances to the deformation region are less than a preset distance threshold are merged as potential deformation slopes.

5. The method according to claim 1, characterized in that For any potential deformation slope, judging whether the potential deformation slope is reasonable based on the comprehensive layer includes: Determine the texture, optical features and buildings of the potential deformable slope based on the target optical image in the comprehensive layer, and determine the terrain parameters of the potential deformable slope based on the three-dimensional terrain data in the comprehensive layer; Based on the texture and optical characteristics of the potential deformation slope, determining whether the potential deformation slope is optically reasonable; Based on the building of the potential deformation slope, judging whether the potential deformation slope has architectural rationality; Based on the terrain parameters of the potential deformable slope, determining whether the potential deformable slope has terrain rationality; In the case where the potential deformation slope has optical rationality, architectural rationality and topographic rationality, determining that the potential deformation slope has rationality; Alternatively, when the potential deformation slope does not satisfy any one of optical rationality, architectural rationality and terrain rationality, it is determined that the potential deformation slope is not rational.

6. The method according to claim 1, characterized in that The step of acquiring the target optical image, three-dimensional terrain data and SAR image data of the target area includes: Acquire multiple historical optical images of the target area during a preset research period; Determining a target optical image that meets the target research requirements from the multiple historical optical images; Based on the time corresponding to the target optical image, acquiring three-dimensional terrain data of the target area; Acquire a SAR database of the target area; The SAR image data is acquired from the SAR database based on the surface characteristics of the target area, the preset research period and the availability of SAR image data.

7. The method according to claim 1, characterized in that In the case where the potential deformation slope is reasonable, after marking the boundary of the potential deformation slope in the comprehensive layer, the method further includes: Determine the influence area of ​​the potential deformable slope based on the target optical image and the three-dimensional terrain data in the comprehensive layer, wherein the influence area represents the area affected when a landslide disaster occurs on the potential deformable slope; The impact area of ​​the potential deformable slope is marked in the comprehensive layer.

8. A deformation slope extraction device based on multi-source data, characterized in that: The device comprises: An acquisition module is used to acquire target optical images, three-dimensional terrain data and SAR image data of the target area; A superposition module, used for superimposing the target optical image, three-dimensional terrain data and SAR image data of the target area to obtain a comprehensive layer of the target area; A first determination module is used to perform color visualization analysis based on the SAR image data in the comprehensive layer to determine all potential deformation slopes in the target area, wherein the color of the potential deformation slope in the comprehensive layer is different from that of other areas; A judgment module, for judging, for any potential deformation slope, whether the potential deformation slope is reasonable based on the comprehensive layer; The extraction module is used to mark the boundary of the potential deformation slope in the comprehensive layer when the potential deformation slope is reasonable.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the deformation slope extraction method based on multi-source data according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the deformation slope extraction method based on multi-source data according to any one of claims 1 to 7.

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