A method, device and equipment for extracting deformation slope based on multi-source data

Abstract

The present invention relates to the field of geological disaster technology and discloses a method, device, and equipment for extracting deformable slopes based on multi-source data. The method comprises: obtaining a target optical image, three-dimensional terrain data, and SAR image data of a target area; superimposing the above data to obtain a comprehensive layer; performing color visualization analysis based on the SAR image data in the comprehensive layer to determine all potential deformable slopes; for any potential deformable slope, judging whether the potential deformable slope is reasonable based on the comprehensive layer; if reasonable, marking the boundary of the potential deformable slope in the comprehensive layer. The present invention reduces the errors or limitations caused by a single data source by using multiple data sources, and identifies the color difference between the deformable slope and the surrounding area through color visualization analysis to more accurately mark the boundary of the deformable slope, thereby achieving high-precision extraction of the deformable slope and providing a reference basis for geological disaster warning, monitoring, and control.

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] A landslide is the movement of large amounts of rock, soil, or debris down a slope. Frequent landslide disasters can cause significant loss of life and property, severely impacting people's quality of life and sustainable social development. Therefore, research on landslide early warning is crucial. Slope stability is a key focus of landslide early warning research, and therefore, accurate extraction of slope deformation information is a key prerequisite for landslide early warning.

[0003] Currently, deformation slopes can be extracted in many ways, such as extracting deformation slopes based on deformation data. However, 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 method for extracting deformation slopes based on multi-source data, the method comprising:

[0006] Acquire 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] Perform color visualization analysis based on SAR image data in the integrated layer to identify all potential deformation slopes in the target area. The color of the potential deformation slope in the integrated layer is different from that of other areas.

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

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

[0011] The multi-source data-based deformation slope extraction method provided in an 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 brought about by a single data source, thereby improving the reliability and accuracy of the deformation slope extraction results. The multi-source data are superimposed to form a comprehensive layer containing multiple information, which helps to more comprehensively understand the geomorphological characteristics and changes of the target area. Color visualization analysis is then performed in the comprehensive layer, and the color difference between the deformation slope and the surrounding area can be directly observed, facilitating the rapid identification of potential deformation slopes. The rationality of the potential deformation slope is then judged, and some misjudged or unreasonable areas can be eliminated, thereby improving the reliability of deformation slope extraction. Finally, the boundaries of reasonable deformation slopes are accurately marked, achieving high-precision extraction of deformation slopes. The marked deformation slope boundaries provide an important reference basis for subsequent geological disaster warning, monitoring, and control.

[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 cell in the comprehensive layer;

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

[0015] For each grid cell whose deformation value is within a preset display range, determining a display color and a 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;

[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, if 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 small surface deformations through InSAR technology to obtain accurate deformation values. Then, the natural discontinuity method can be used to determine the optimal deformation value interval division scheme based on the deformation value, thereby determining the number of display colors and intuitively displaying areas of different deformation degrees. Then, for each grid cell whose deformation value is within a preset display range, the display color and color value of the grid cell are determined. This 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 embodiment, a natural discontinuity method is used to determine multiple display colors based on the deformation value of each grid cell, including:

[0021] Dividing the deformation value interval based on the deformation value of each grid cell 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] Determines a target number of display colors from a color ramp, 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 deformation slope more intuitive and clear, making it more intuitive.

[0026] In an optional embodiment, a connected component 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 the first mark to the first target grid cell;

[0028] Continue to traverse all target grid cells that have not been marked. 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.

[0029] Alternatively, when the second target grid cell is not 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;

[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 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 cell is correctly marked through the connected region algorithm, and then corrects it after marking. By setting a distance threshold, adjacent deformation areas are reasonably merged, making the extraction of potential deformation slopes more scientific and reliable.

[0035] In an optional embodiment, 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 are architecturally reasonable;

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

[0040] Under the condition that the potential deformation slope has optical rationality, architectural rationality and topographic rationality, the potential deformation slope is determined to be rational;

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

[0042] The multi-source data-based deformable slope extraction method provided in an embodiment of the present invention provides a complete description of potential deformable slopes by collecting data from multiple dimensions. Based on this information, a comprehensive evaluation is then performed in three dimensions: optical, architectural, and topographic. This provides a comprehensive rationality assessment and enhances the credibility of the extraction results.

[0043] In an optional embodiment, 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 multi-source data-based deformation slope extraction method provided by the embodiments of the present invention improves the accuracy and reliability of the analysis results by screening target optical images that meet the research requirements. Then, based on the time corresponding to the target optical image, the corresponding three-dimensional terrain data is obtained and aligned in time and space to facilitate comprehensive analysis. Then, based on the surface characteristics of the target area and the research period, suitable SAR image data is accurately selected. By acquiring 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 embodiment, when the potential deformation slope is reasonable, after marking the boundary of the potential deformation slope in the integrated layer, the method further includes:

[0051] Based on the target optical image and 3D terrain data in the integrated layer, the impact area of the potential deformable slope is determined. The impact area represents the area affected by the potential deformable slope when a landslide hazard occurs.

[0052] Mark the impact area of potential deformable slopes 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 of a potential deformable slope when a landslide occurs through comprehensive analysis of multi-source data, providing a scientific basis for subsequent risk assessment and improving the scientific nature 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. 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 any potential deformation slope is reasonable based on the comprehensive layer map;

[0059] The extraction module is used to mark the boundaries of potential deformation slopes in the comprehensive layer if 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 computer instructions to thereby execute the deformation slope extraction method based on multi-source data of the above-mentioned first aspect or any corresponding embodiment thereof.

[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 above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any 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 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying 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 making creative efforts shall fall within the scope of protection of the present invention.

[0069] Related technologies extract deformation slopes based on deformation data, but their considerations are too single, resulting in incomplete extraction and general accuracy. Algorithms can also be relied upon to automatically extract deformation slopes, but their 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 brought about by a single data source, thereby improving the reliability and accuracy of the deformation slope extraction results. Color visualization analysis can be performed to directly observe the color difference between the deformation slope and the surrounding area, facilitating the rapid identification of potential deformation slopes, and then judging 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 basis for subsequent geological disaster warning, monitoring and control.

[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, Figure 1 FIG. 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 the 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, buildings, etc. The SAR (Synthetic Aperture Radar) image data is a microwave remote sensing image that reflects surface features through phase difference and intensity changes. The data source can be selected by itself to achieve data acquisition. Since the relevant technology only considers single data when extracting deformation slopes, resulting in low extraction accuracy, the embodiment of the present invention obtains multi-source data for deformation slope extraction to improve extraction accuracy.

[0073] In step S102, the target optical image, 3D terrain data, and SAR image data of the target area are superimposed to generate a comprehensive layer of the target area. Specifically, these three types of data are imported into a GIS (Geographic Information System) platform. By adjusting the transparency of the different data layers, the layers are superimposed to generate a comprehensive layer of the target area. This layer can intuitively display various surface features of the target area, providing a foundation for subsequent deformation slope extraction.

[0074] In step S103, color visualization analysis is performed on the integrated layer based on the SAR image data to identify all potential deformation slopes in the target area. Potential deformation slopes are displayed in a different color than other areas in the integrated layer. Specifically, the phase information of the SAR image data is used to obtain the deformation value of each grid cell in the integrated layer. A different color is then assigned to each grid cell based on the deformation value. Color visualization facilitates rapid identification and extraction of deformation slopes.

[0075] In step S104, for each potential deformation slope, the rationality of the potential deformation slope is determined based on the comprehensive layer. Specifically, because 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. This ensures that the extracted potential deformation slope is reasonable and reduces misjudgments.

[0076] In step S105, if the potential deformable slope is reasonable, the boundary of the potential deformable slope is annotated in the comprehensive layer. Specifically, in the comprehensive layer of the GIS platform, vector tools, such as white lines, are used to accurately locate and draw the reasonable boundary of the potential deformable slope. This annotation allows for intuitive display of potential deformable slope information, enabling accurate extraction of the deformable slope and providing important decision-making basis for disaster warning and emergency response.

[0077] The multi-source data-based deformation slope extraction method provided in an 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 brought about by a single data source, thereby improving the reliability and accuracy of the deformation slope extraction results. The multi-source data are superimposed to form a comprehensive layer containing multiple information, which helps to more comprehensively understand the geomorphological characteristics and changes of the target area. Color visualization analysis is then performed in the comprehensive layer, and the color difference between the deformation slope and the surrounding area can be directly observed, facilitating the rapid identification of potential deformation slopes. The rationality of the potential deformation slope is then judged, and some misjudged or unreasonable areas can be eliminated, thereby improving the reliability of deformation slope extraction. Finally, the boundaries of reasonable deformation slopes are accurately marked, achieving high-precision extraction of deformation slopes. The marked deformation slope boundaries provide an important reference basis for subsequent geological disaster warning, monitoring, and control.

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

[0079] Step S201 : acquiring 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 involves acquiring multiple historical optical images of the target area over a pre-set research period. Specifically, these historical optical images depict surface features of the target area at different times. For example, if researchers are extracting deformed slopes on December 10th, the pre-set research period should be as close to that date as possible to ensure research timeliness. For example, a two-day period could be used, meaning all historical optical images from December 8th and December 9th would be acquired 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 can be that the surface features are most obvious, the interference factors are the least, and the amount of information is the largest. For example, the vegetation has a smaller impact to better identify other surface features; the snow and cloud cover in the image are as little as possible to avoid affecting the visibility of surface details; the lighting conditions are good to ensure the clarity and contrast of the image. Optionally, the target research requirements can 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: Acquire 3D topographic data of the target area based on the time corresponding to the target optical image. Specifically, assuming the target optical image was captured at 3:00 PM on December 10th, acquire 3D topographic data of the target area at the time closest to 3:00 PM on December 10th to achieve temporal and spatial alignment.

[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, and this embodiment of the present invention does not limit this.

[0085] Step S2015: SAR image data is obtained from the SAR database based on the target area's surface characteristics, the pre-set research period, and the availability of SAR image data. Specifically, by comprehensively considering the target area's surface characteristics, the pre-set research period, and data availability, the target area's surface characteristics are analyzed to determine suitable data. Based on the requirements of the pre-set research period, data with complete coverage is selected. The data quality and applicability are also evaluated to ensure that it meets the research requirements, ultimately obtaining the SAR image data.

[0086] Step S202: superimpose the target optical image, 3D 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] In step S203 , color visualization analysis is performed on the integrated layer based on the SAR image data to determine all potential deformation slopes in the target area. The color of the potential deformation slopes in the integrated layer is different from that of other areas.

[0088] Specifically, the above step S203 includes:

[0089] Step S2031: InSAR technology is used to process the SAR image data to obtain deformation values for each grid cell in the integrated layer. Specifically, there are multiple types of InSAR technology, each with different technical advantages. For example, Differential-InSAR (D-InSAR) technology has the advantage of a simple and intuitive data processing process. However, it relies on highly repeatable SAR image data and is suitable for monitoring short-term surface changes. Its sensitivity is relatively low, making it unsuitable for large-scale deformation monitoring. Small Baseline Subset-InSAR (SBAS-InSAR) technology can achieve high monitoring accuracy and is suitable for medium-scale surface deformation monitoring. However, this technology requires a large amount of SAR image data and the data processing process is relatively complex. Persistent Scatterer-InSAR (PS-InSAR) technology can obtain high-precision and high-sensitivity monitoring results and is suitable for monitoring complex terrain and nonlinear deformation. However, its data processing is also complex and requires a large amount of SAR image data. It also requires that the surface scatterers have high stability. Based on the researcher's research objectives and the accuracy and resolution requirements for 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 cell in the integrated layer.

[0090] In step S2032 , a natural discontinuity method is used to determine a plurality of display colors based on the deformation value of each grid cell.

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

[0094] In step a3, the number of deformation value intervals included in the partitioning scheme corresponding to the minimum total variance is determined as the target number. Specifically, the total variances of different partitioning schemes are compared, and the partitioning scheme with the minimum total variance is selected. The number of deformation value intervals in this scheme is the target number.

[0095] Step a4: Determine a target number of display colors from the color band, the target number of display colors including both cool and warm colors. Specifically, the target number of display colors is selected from a predefined color band. Typically, the display colors include both cool and warm colors, with cool colors representing grid cells with negative deformation values to indicate surface subsidence or collapse, and warm colors representing grid cells with positive deformation values to indicate surface rise or uplift, thereby visually distinguishing the surface conditions of different grid cells.

[0096] In step S2033, for each grid cell whose deformation value falls within a preset display range, the grid cell's display color and color value are determined based on the grid cell's deformation value. The color value represents the grid cell's display color and its depth. Specifically, the preset display range is the deformation value range. Only grid cells with deformation values within this range are displayed in color. Grid cells outside this range are displayed without color or with a default color. Assuming the target number obtained in the above steps is 2, two display colors are selected from the color band: green (cold tones) and brown (warm tones). For any two grid cells with deformation values of -0.15 and -0.3, respectively, 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, a grid cell with a deformation value of -0.3 should be darker green than a grid cell with a deformation value of -0.15. Similarly, for any two grid cells with deformation values of 0.2 and 0.25, respectively, both should be colored brown. The larger the positive deformation value, the darker the color, indicating greater surface uplift or bulge. 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 (Red, Green, Blue) value, which represents the color of the grid cell and its degree of lightness or darkness.

[0097] In 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 integrated layer. Specifically, based on the color value of each grid cell obtained in step S2032, a corresponding color is set in the integrated layer to intuitively display the deformation of each location within the target area.

[0098] In step S2035, for each grid cell, if 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 identified as a target grid cell. Specifically, connectivity is defined as 4-connectivity and 8-connectivity. 4-connectivity refers to a grid cell being connected to its four neighboring cells above, below, left, and right, while 8-connectivity refers to a grid cell being connected to its eight surrounding neighboring cells. This embodiment of the present invention uses 4-connectivity as an example for illustration; other connectivity schemes may be employed in practice and are not limited in this embodiment. Because different color values represent different display colors and color depths, and thus represent the magnitude and sign of deformation values, if the color value of a region is significantly higher than that of the surrounding regions, it indicates that its deformation value is significantly higher than that of the surrounding regions, and is more likely to be a deformation slope. Therefore, for any grid cell with four connected cells, the difference in color value between the grid cell and each connected cell is determined. Cells with a difference greater than the preset difference threshold are identified as target cells and serve as the focus for deformation slope extraction.

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

[0100] In some optional implementations, 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), the first target grid cell is marked with a first mark of "1". Optionally, a default mark of "0" can be added to grid cells that are not target cells, and target grid cells that have not been traversed are not marked for the time being.

[0102]

[0103] Step b2: Continue traversing all unmarked target grid cells. 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. Specifically, continue traversing the unmarked target grid cells. If the next target grid cell is 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, if the second target grid cell is not a connected cell of any target grid cell, a second tag is added to the second target grid cell, where the second tag is obtained by accumulating the first tag. Specifically, judging the target grid cell in the first row and sixth column in the above formula (1), it is known that it is not a connected cell of any target grid cell. Therefore, a second tag is obtained by accumulating the first tag and adding the second tag "2" to it, resulting in the following formula (2).

[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 in 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 in 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 side and left side 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] In step b5, the mark of each target grid cell is corrected to the minimum mark among the marks of all connected cells of the target grid cell. Specifically, since some connected cells of the target grid cell may not be 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 in step b4, the mark of each target grid cell needs to be corrected to make the connected region analysis more accurate. The correction is made by referring to the method used when marking the target grid cell in the second row and third column, so that the mark of each target grid cell is minimized. The final marking result is shown in the following equation (4).

[0109]

[0110] In step b6, target grid cells with consistent markings and continuous positions are treated as a deformation region to obtain at least one deformation region. Specifically, target grid cells with consistent markings and continuous positions are treated as a deformation region, and two deformation regions labeled "1" and "2" can be obtained from equation (4). Optionally, there are no positionally discontinuous, i.e., independent, target grid cells in equation (4), and therefore they are not considered. In practical applications, such target grid cells can be discarded.

[0111] In step b7, for any deformed region, the deformed region and all deformed regions whose distance to the deformed region is less than a preset distance threshold are merged to form a potential deformation slope. Specifically, for any deformed region, all deformed regions whose distance to the deformed region is less than the preset distance threshold are merged to form a potential deformation slope. This merging operation can produce larger, continuous deformation slope regions, further improving the accuracy of deformation slope extraction and 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 determines the texture, optical characteristics, and buildings of the potential deformable slope based on the target optical image in the comprehensive layer, and determines the terrain parameters of the potential deformable slope 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 refers to the texture characteristics of the slope surface, such as roughness and crack distribution, 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 buildings are buildings located on the slope; the terrain parameters include elevation, slope, aspect, etc., which describe the morphology and geographical environment of the slope and help to assess the stability of the slope.

[0115] In step S2042, based on the texture and optical characteristics of the potential deformation slope, a determination is made as to whether the potential deformation slope is optically reasonable. Specifically, the texture and optical characteristics of the potential deformation slope are compared with the actual deformation slope to determine their optical reasonableness. For example, if no physical changes in the actual deformation slope are found in the texture, then there may be irrationality.

[0116] In step S2043, based on the buildings on the potential deformable slope, it is determined whether the potential deformable slope has architectural rationality. Specifically, since buildings cannot exist on a deformable slope, if the optical image data indicates that a building exists on the potential deformable slope, the potential deformable slope does not have architectural rationality and is likely not a true deformable slope.

[0117] Step S2044: Based on the terrain parameters of the potential deformable slope, determine whether the potential deformable slope is terrain reasonable. Specifically, combine geomorphological principles to determine whether the terrain parameters of the potential deformable slope conform to the geometric characteristics of the actual slope to determine its terrain reasonableness.

[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 all three rationalities, it is considered to be more likely to be a real deformable slope.

[0119] Alternatively, in step S2046, if the potential deformable slope does not meet any of the rationality criteria of optical rationality, architectural rationality, and topographic rationality, the potential deformable slope is determined to be not rational. Specifically, if a potential deformable slope does not meet any of the above rationality criteria, it is considered less likely to be a true deformable slope.

[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, assuming the number of targets obtained using the natural discontinuity method in step S2032 is two, two display colors are selected from the color band: green and brown, to visually display the target area. The potential deformation slope is marked with a white line, and different shades of green represent different degrees of subsidence 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 10. Figure 3 The ten display colors shown on the left provide a visual representation of the target area. Potentially deformable slopes are marked with white lines, and different deformation values correspond to different display colors and shades. By marking potential deformable slopes in the integrated layer, accurate extraction of deformable slopes is achieved, helping to visually display their locations. It also allows understanding the degree of deformation at different locations on the slope and observing the degree of deformation in other areas of the target area, providing an important reference for subsequent geological disaster warning, monitoring, and control.

[0122] In step S206, the impact area of the potential deformable slope is determined based on the target optical image and 3D terrain data in the integrated layer. The impact area represents the area that would be affected if a landslide occurred on the potential deformable slope. Specifically, the target optical image is used to analyze the vegetation cover, soil erosion, and human activities surrounding the potential deformable slope. Simultaneously, using the 3D terrain model, combined with the potential slope's slope, aspect, elevation, and other terrain parameters and geomorphological principles, the area potentially affected by a landslide on the potential deformable slope is determined.

[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 hazard assessment and subsequent research and prevention needs.

[0124] The multi-source data-based deformation slope extraction method provided in an 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 brought about by a single data source, thereby improving the reliability and accuracy of the deformation slope extraction results. The multi-source data are superimposed to form a comprehensive layer containing multiple information, which helps to more comprehensively understand the geomorphological characteristics and changes of the target area. Color visualization analysis is then performed in the comprehensive layer, and the color difference between the deformation slope and the surrounding area can be directly observed, facilitating the rapid identification of potential deformation slopes. The rationality of the potential deformation slope is then judged, and some misjudged or unreasonable areas can be eliminated, thereby improving the reliability of deformation slope extraction. Finally, the boundaries of reasonable deformation slopes are accurately marked, achieving high-precision extraction of deformation slopes. The marked deformation slope boundaries provide an important reference basis for subsequent geological disaster warning, monitoring, and control.

[0125] This embodiment also provides a deformation slope extraction device based on multi-source data, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

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

[0127] The acquisition module 401 is used to acquire target optical images, 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 determining module 403 is configured 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. The color of the potential deformation slope in the comprehensive layer is different from that of other areas.

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

[0131] The extraction module 405 is configured to mark the boundary of the potential deformation slope in the comprehensive layer if 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 determining unit is configured 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, where 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 based on the color value of the grid cell.

[0137] The third determining unit is configured to determine, for each grid cell, if 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, determine the grid cell as a target grid cell.

[0138] The fourth determining unit is configured 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 sub-units are divided 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 configured 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 determining subunit is configured to determine a target number of display colors from the color band, where the target number of display colors includes both cool colors and warm 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 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.

[0147] Alternatively, the second marking subunit is configured 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, where the second mark is accumulated by adding the first marks.

[0148] The second marking subunit is used to 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.

[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 configured 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 configured 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 determining unit is configured 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 configured 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 with the potential deformation slope.

[0156] The third judgment unit is configured 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 configured to determine whether the potential deformable slope is reasonable when the potential deformable slope is optically reasonable, architecturally reasonable, and topographically reasonable.

[0158] Alternatively, the seventh determining unit is configured to determine that the potential deformation slope is not rational when the potential deformation slope does not satisfy any one of optical rationality, architectural rationality, and topographic 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 determining unit is configured to determine a target optical image that meets target research requirements from a plurality of historical optical images.

[0162] The second acquiring unit is configured 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 configured to acquire a SAR database of the target area.

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

[0165] In some optional implementations, after the extraction module 405, the apparatus 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. The impact area represents the area affected when a landslide disaster occurs on the potential deformable slope.

[0167] The annotation module is used to mark 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 structural diagram 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, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other 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 the memory 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. Equally, multiple computer devices can be connected, and each device provides part of the 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 an application-specific 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 that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute 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 and application programs required for at least one function; the data storage area may store data created based on 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 located 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 further 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 bus connection is taken as an example.

[0177] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). 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 above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, 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 drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. 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 portion 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 form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. 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 that can be accessed by the computer.

[0180] Although the embodiments of the present invention have been described with reference to 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. 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: Acquire 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 map; If the potential deformation slope is reasonable, marking the boundary of the potential deformation slope in the comprehensive layer; The color visualization analysis based on the SAR image data is performed in the comprehensive layer 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 cell in the comprehensive layer; Using the natural discontinuity method, multiple display colors are determined based on the deformation value of each grid cell; For each grid cell whose deformation value is within a preset display range, determining a display color and a 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 shade; 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 integrated layer; For each grid cell, if 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; The connected region algorithm is used to determine the potential deformation slope of the target area based on all target grid cells.

2. The method according to claim 1, characterized in that The natural discontinuity method is used to determine multiple display colors based on the deformation value of each grid cell, including: Dividing the deformation value interval based on the deformation value of each grid cell 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 colors and warm colors.

3. The method according to claim 1, 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 the first mark to the first target grid cell; Continue to traverse all target grid cells that have not been marked. 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. Alternatively, when the second target grid cell is not 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 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.

4. The method according to claim 1, wherein For any potential deformation slope, judging whether the potential deformation slope is reasonable based on the comprehensive layer map includes: Determining the texture, optical characteristics, and buildings of the potential deformable slope based on the target optical image in the comprehensive layer, and determining the terrain parameters of the potential deformable slope based on the three-dimensional terrain data in the comprehensive layer; determining whether the potential deformation slope is optically reasonable based on the texture and optical characteristics of the potential deformation slope; Based on the building on the potential deformation slope, determining 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; When 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 topographic rationality, it is determined that the potential deformation slope is not rational.

5. The method according to claim 1, characterized in that The step of acquiring target optical images, 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 target research requirements from the plurality of historical optical images; acquiring three-dimensional terrain data of the target area based on the time corresponding to the target optical image; Acquiring a SAR database of the target area; The SAR image data is obtained from the SAR database based on the surface characteristics of the target area, the preset research period and the availability of SAR image data.

6. 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 integrated layer, the method further includes: Determining an impact area of the potential deformable slope based on the target optical image and three-dimensional terrain data in the integrated layer, wherein the impact area represents an 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.

7. 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 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 determining module is configured 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, configured to judge, for any potential deformation slope, whether the potential deformation slope is reasonable based on the comprehensive map; an extraction module, configured to mark the boundary of the potential deformation slope in the comprehensive layer if the potential deformation slope is reasonable; The first determining module is specifically configured to: The SAR image data is processed using InSAR technology to obtain a deformation value of each grid cell in the comprehensive layer; Using the natural discontinuity method, multiple display colors are determined based on the deformation value of each grid cell; For each grid cell whose deformation value is within a preset display range, determining a display color and a 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 shade; 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 integrated layer; For each grid cell, if 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; The connected region algorithm is used to determine the potential deformation slope of the target area based on all target grid cells.

8. 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 6 by executing the computer instructions.

9. 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 6.

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