Geological exploration method using integrated remote sensing image processing technology

Abstract

The present invention relates to the technical field of image processing, and particularly to a geological exploration method integrating remote sensing image processing technology, including: obtaining a plurality of suspected landslide areas in the geological remote sensing image in the real-time stage; obtaining the surface quality differentiation degree of each suspected landslide area according to the geological whitening degree, the geological position offset degree, and the gray-scale difference of pixel points in each suspected landslide area; obtaining the conformity degree of landslide deformation characteristics of each suspected landslide area according to the distribution of height data of pixel points in the geological remote sensing image of each suspected landslide area in the real-time stage and the geological remote sensing image of its adjacent sampling stage; obtaining the landslide hazard tendency index of each suspected landslide area according to the conformity degree of landslide deformation characteristics and the landslide surface quality presentation degree; and prospecting the landslide hazard areas in the geological remote sensing image in the real-time stage based on the landslide hazard tendency index. The present invention improves the accuracy of identifying landslide hazard areas in remote sensing images.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a geological exploration method integrating remote sensing image processing technology. Background Art

[0002] Landslides are the most frequent and most damaging geological natural disasters globally. Geological warning information will be issued before a landslide occurs. For example, geological instability deformation occurs, resulting in uneven surface stress. If rainfall or engineering construction is carried out in the landslide-prone area, a landslide disaster is likely to occur. Therefore, by using remote sensing images to explore and monitor the landslide-prone area based on landslide characteristics, it can effectively improve people's timeliness in dealing with landslides and minimize the disaster losses caused by landslides; in the traditional process of identifying landslide-prone areas based on optical remote sensing images, an unmanned aerial vehicle is used to collect remote sensing images of the monitored mountain area, and a trained neural network model is used to identify each landslide-prone area in the remote sensing image. In the existing neural network training and identification process of mountain remote sensing images, the identification of landslide-prone areas is mainly based on the similarity matching of collapse cracks within the area. However, due to the very different mountain contours in the actual remote sensing image and the complex deep edge morphology inside, the matching difficulty between the existing neural network crack training set and the crack edges in the actual remote sensing image is relatively large, resulting in inaccurate identification of the landslide-prone areas in the remote sensing image. Summary of the Invention

[0003] In order to solve the above problems, the present invention provides a geological exploration method integrating remote sensing image processing technology, and the method includes:

[0004] Obtain geological remote sensing images of several acquisition stages, and height data of each pixel point in the geological remote sensing images of each acquisition stage;

[0005] Record the geological remote sensing image of the last acquisition stage as the geological remote sensing image of the real-time stage; according to the difference in height data of each pixel point in the geological remote sensing image of the real-time stage and its corresponding pixel points in the geological remote sensing images of other acquisition stages, obtain the geological position offset degree of each pixel point in the geological remote sensing image of the real-time stage; cluster the geological remote sensing image of the real-time stage according to the geological position offset degree of the pixel points to obtain several suspected landslide areas in the geological remote sensing image of the real-time stage; according to the gray distribution of the pixel points in each suspected landslide area, obtain the geological whiteness degree of each suspected landslide area; according to the geological whiteness degree, the geological position offset degree, and the gray difference of the pixel points in each suspected landslide area, obtain the surface quality differentiation degree of each suspected landslide area;

[0006] Obtain the landslide surface quality presentation degree of each suspected landslide area according to the surface quality differentiation degree in the geological remote sensing images of each suspected landslide area at different sampling stages; obtain the landslide deformation feature compliance degree of each suspected landslide area according to the distribution of the height data of the pixel points in the geological remote sensing image of the real-time stage and the geological remote sensing images of its adjacent sampling stages; obtain the landslide hazard tendency index of each suspected landslide area according to the landslide deformation feature compliance degree and the landslide surface quality presentation degree;

[0007] Explore the landslide hazard areas in the geological remote sensing image of the real-time stage based on the landslide hazard tendency index.

[0008] Preferably, the method for obtaining the geological position offset degree of each pixel point in the geological remote sensing image of the real-time stage according to the difference in the height data of each pixel point in the geological remote sensing image of the real-time stage and its height data in the geological remote sensing images of other acquisition stages includes the following specific method:

[0009] For the th pixel point in the geological remote sensing image of the real-time stage and any one acquisition stage's geological remote sensing image other than the geological remote sensing image of the real-time stage, record the absolute value of the difference between the height data of the th pixel point in the geological remote sensing image of the real-time stage and the height data of the th pixel point in the any one acquisition stage's geological remote sensing image as the height difference value of the th pixel point in the any one acquisition stage's geological remote sensing image; take the average value of the height difference values of the th pixel point in all acquisition stage's geological remote sensing images other than the geological remote sensing image of the real-time stage as the geological position offset degree of the th pixel point in the geological remote sensing image of the real-time stage.

[0010] Preferably, the method for clustering the geological remote sensing image of the real-time stage according to the geological position offset degree of the pixel points to obtain several suspected landslide areas in the geological remote sensing image of the real-time stage includes the following specific method:

[0011] Input the geological position offset degrees of all pixel points in the geological remote sensing image of the real-time stage into the DBSCAN density clustering algorithm to cluster all pixel points in the geological remote sensing image of the real-time stage, and obtain several clustering clusters; in the geological remote sensing image of the real-time stage, record the area formed by all pixel points in each clustering cluster as a suspected landslide area.

[0012] Preferably, the method for obtaining the geological whitening degree of each suspected landslide area according to the gray-scale distribution of the pixel points in each suspected landslide area includes the following specific method:

[0013] For the th suspected landslide area in the geological remote sensing image of the real-time stage, the difference between the mean value of the gray values of all pixel points in the th suspected landslide area and the minimum value of the gray values of all pixel points in the th suspected landslide area is denoted as the first difference; the difference between the maximum value of the gray values of all pixel points in the th suspected landslide area and the minimum value of the gray values of all pixel points in the th suspected landslide area is denoted as the first extreme value; the ratio of the first difference to the first extreme value is used as the geological whiteness degree of the th suspected landslide area.

[0014] Preferably, the method for obtaining the surface quality differentiation degree of each suspected landslide area according to the geological whiteness degree, the geological position deviation degree, and the gray value difference situation of the pixel points in each suspected landslide area includes the following specific method:

[0015] The absolute value of the difference between the gray value of the th pixel point in the th suspected landslide area and the mean value of the gray values of all pixel points in the th suspected landslide area is denoted as the gray value difference of the th pixel point; the mean value of the gray value differences of all pixel points in the th suspected landslide area is denoted as the gray value difference of the th suspected landslide area; the normalized value of the product of the gray value difference of the th suspected landslide area, the geological whiteness degree of the th suspected landslide area, and the mean value of the geological position deviation degrees of all pixel points in the th suspected landslide area is used as the surface quality differentiation degree of the th suspected landslide area.

[0016] Preferably, the method for obtaining the landslide surface quality presentation degree of each suspected landslide area according to the surface quality differentiation degree of each suspected landslide area in the geological remote sensing images at different sampling stages includes the following specific method:

[0017] Taking the serial number of the sampling stage as the abscissa and the surface quality differentiation degree as the ordinate to construct a two-dimensional coordinate system, inputting the surface quality differentiation degrees of the th suspected landslide area in the geological remote sensing images at all sampling stages into the two-dimensional coordinate system, and using the least squares method for curve fitting to obtain the surface quality differentiation change curve of the th suspected landslide area;

[0018] Decompose the surface mass differentiation change curve of the th suspected landslide area using the STL trend item decomposition method to obtain the trend item curve of the th suspected landslide area;

[0019] Take the mean value of the slopes of all data points on the trend item curve of the th suspected landslide area as the mean value of the trend item slope of the th suspected landslide area; Take the ratio between the mean value of the trend item slope of the th suspected landslide area and the maximum value of the mean values of the trend item slopes of all suspected landslide areas as the trend item slope ratio; Multiply the trend item slope ratio by the surface mass differentiation degree of the th suspected landslide area in the geological remote sensing image in the real-time stage as the landslide surface mass presentation degree of the th suspected landslide area.

[0020] Preferably, the method for obtaining the landslide deformation feature compliance degree of each suspected landslide area according to the distribution of the height data of the pixel points in the geological remote sensing image of each suspected landslide area in the real-time stage and the geological remote sensing image of its adjacent sampling stage includes the following specific methods:

[0021] Take the geological remote sensing image of the previous acquisition stage of the geological remote sensing image in the real-time stage as the geological remote sensing image in the comparison stage;

[0022] According to the distribution difference of the height data of the pixel points in the geological remote sensing image of the th suspected landslide area in the real-time stage and the geological remote sensing image in the comparison stage, obtain the deformation subsidence ratio of the target pixel points of the th suspected landslide area;

[0023] Take the difference between the maximum value of the height data of all pixel points in the th suspected landslide area and the height data of the target pixel points of the th suspected landslide area as the deformation height difference. Take the product of the deformation subsidence ratio of the target pixel points of the th suspected landslide area and the reciprocal of the deformation height difference as the landslide deformation feature compliance degree of the th suspected landslide area.

[0024] Preferably, the method for obtaining the deformation subsidence ratio of the target pixel points of the th suspected landslide area according to the distribution difference of the height data of the pixel points in the geological remote sensing image of the th suspected landslide area in the real-time stage and the geological remote sensing image in the comparison stage includes the following specific methods:

[0025] In the geological remote sensing image in the real-time stage, in the th suspected landslide area, the pixel point with the maximum height data is recorded as the height maximum pixel point of the th suspected landslide area; the pixel point with the minimum height data is recorded as the height minimum pixel point of the th suspected landslide area; the straight line connection between the height maximum pixel point of the th suspected landslide area and the height minimum pixel point of the th suspected landslide area is recorded as the soil flow direction straight line of the th suspected landslide area;

[0026] For the th pixel point on the soil flow direction straight line of the th suspected landslide area, use a line passing through the th pixel point and perpendicular to the soil flow direction straight line of the th suspected landslide area to divide the th suspected landslide area into an upper area and a lower area;

[0027] Record the average value of the height data of all pixel points in the lower area of the th suspected landslide area in the geological remote sensing image in the real-time stage as the first height average value; record the average value of the height data of all pixel points in the lower area of the th suspected landslide area in the geological remote sensing image in the comparison stage as the second height average value; record the absolute value of the difference between the first height average value and the second height average value as the convexity degree of the lower area of the th pixel point;

[0028] Record the average value of the height data of all pixel points in the upper area of the th suspected landslide area in the geological remote sensing image in the real-time stage as the third height average value; record the average value of the height data of all pixel points in the upper area of the th suspected landslide area in the geological remote sensing image in the comparison stage as the fourth height average value; record the absolute value of the difference between the third height average value and the fourth height average value as the concavity degree of the upper area of the th pixel point;

[0029] Record the ratio of the concavity degree of the upper area of the th pixel point to the convexity degree of the lower area of the th pixel point as the deformation convexity-concavity ratio of the th pixel point;

[0030] Among all pixel points on the soil flow direction straight line of the th suspected landslide area, record the pixel point corresponding to the maximum deformation convexity-concavity ratio as the Target pixel points in a suspected landslide area.

[0031] Preferably, obtaining the landslide hazard tendency index for each suspected landslide area according to the landslide deformation feature conformity and the landslide surface quality presentation degree includes the following specific method:

[0032] Multiply the landslide deformation feature conformity of the th suspected landslide area by the landslide surface quality presentation degree of the th suspected landslide area as the landslide hazard reflection degree of the th suspected landslide area;

[0033] Take the normalized value of the ratio between the landslide hazard reflection degree of the th suspected landslide area and the average value of the landslide hazard reflection degrees of all suspected landslide areas as the landslide hazard tendency index of the th suspected landslide area.

[0034] Preferably, prospecting the landslide hazard areas in the geological remote sensing image in the real-time stage based on the landslide hazard tendency index includes the following specific method:

[0035] Preset two threshold parameters and . For any suspected landslide area, if the landslide hazard tendency index of the any suspected landslide area is less than the threshold parameter , mark the any suspected landslide area as a low landslide risk area; if the landslide hazard tendency index of the any suspected landslide area is greater than the threshold parameter , mark the any suspected landslide area as a high landslide risk area.

[0036] The beneficial effects of the technical solution of the present invention are as follows: The present invention clusters the geological remote sensing images in the real-time stage according to the geological position offset degree of pixel points, and obtains several suspected landslide areas in the geological remote sensing images in the real-time stage; obtains the geological whitening degree of each suspected landslide area; obtains the surface geology differentiation degree of each suspected landslide area according to the geological whitening degree, the geological position offset degree, and the gray-scale difference of pixel points in each suspected landslide area; obtains the landslide surface geology presentation degree of each suspected landslide area; obtains the landslide deformation feature conformity degree of each suspected landslide area according to the distribution of the height data of pixel points in the geological remote sensing image of each suspected landslide area in the real-time stage and the geological remote sensing image of its adjacent sampling stage; obtains the landslide hidden danger tendency index of each suspected landslide area according to the landslide deformation feature conformity degree and the landslide surface geology presentation degree; and explores the landslide hidden danger areas in the geological remote sensing image in the real-time stage based on the landslide hidden danger tendency index. In this way, it is possible to combine the performance characteristics of landslides in geological remote sensing images and stress deformation characteristics to obtain more accurate landslide hidden danger areas, thereby improving the accuracy of identifying landslide hidden danger areas in remote sensing images. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is a flowchart of the steps of the geological exploration method for the integrated remote sensing image processing technology of the present invention;

[0039] Figure 2 It is a flowchart of the characteristic relationship of the geological exploration method for the integrated remote sensing image processing technology of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, characteristics, and effects of the geological exploration method for the integrated remote sensing image processing technology proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0042] The following specifically describes the specific solution of the geological exploration method of the integrated remote sensing image processing technology provided by the present invention in conjunction with the accompanying drawings.

[0043] Please refer to Figure 1 , which shows a flowchart of the steps of the geological exploration method of the integrated remote sensing image processing technology provided by an embodiment of the present invention. The method includes the following steps:

[0044] Step S001: Obtain geological remote sensing images of several acquisition stages, and height data of each pixel point in the geological remote sensing images of each acquisition stage.

[0045] It should be noted that unstable mountain structures are more likely to cause landslides in harsh environments or engineering construction. When a landslide occurs, the surface soil of the geology deforms under unstable stress and collapses towards the bottom of the mountain, resulting in a large amount of high-risk disaster losses; since landslides often have a translational manifestation, that is, the upper area collapses, and the collapsed soil accumulates and bulges in the lower area, so multiple collapse cracks will appear in the upper and middle areas of the landslide hidden danger area. The greater the crack stretching in the area, the higher the probability of landslide in this area. Therefore, in the existing neural network training and recognition process of mountain remote sensing images, the identification of landslide hidden danger areas mainly relies on the similarity matching of collapse cracks in the area; and due to the widely different mountain contours in the remote sensing images in the actual scene and the complex internal deep edge morphology, it is difficult to match the existing neural network crack training set with the crack edges in the actual remote sensing images. Therefore, in this embodiment, the characteristic performance of landslides in remote sensing images and the law of landslide stress deformation in the actual scene are combined and analyzed to identify more accurate landslide hidden danger areas in mountain remote sensing images.

[0046] Specifically, first, it is necessary to collect geological remote sensing images of several acquisition stages, and height data of each pixel point in the geological remote sensing images of each acquisition stage. The specific process is as follows:

[0047] Each month is an acquisition stage. Each time, the drone collects optical remote sensing images of the monitored mountain area, and performs median filtering denoising and defogging operations on the collected optical remote sensing images. After collecting for one year, geological remote sensing images of several acquisition stages are obtained; at the same time, during each acquisition stage, the radar system of the drone is used to obtain the height data of each pixel point in the geological remote sensing images of each acquisition stage; among them, median filtering denoising and defogging operations are prior arts, and will not be elaborated here in this embodiment.

[0048] So far, geological remote sensing images of several acquisition stages, and height data of each pixel point in the geological remote sensing images of each acquisition stage are obtained through the above method.

[0049] Step S002: Denote the geological remote sensing image of the last acquisition stage as the geological remote sensing image of the real-time stage; according to the difference in height data of each pixel point in the geological remote sensing image of the real-time stage and its corresponding pixel point in the geological remote sensing images of other acquisition stages, obtain the geological position offset degree of each pixel point in the geological remote sensing image of the real-time stage; cluster the geological remote sensing image of the real-time stage according to the geological position offset degree of the pixel points to obtain several suspected landslide areas in the geological remote sensing image of the real-time stage; according to the gray-scale distribution of the pixel points in each suspected landslide area, obtain the geological whitening degree of each suspected landslide area; according to the geological whitening degree, the geological position offset degree, and the gray-scale difference of the pixel points in each suspected landslide area, obtain the surface geology differentiation degree of each suspected landslide area.

[0050] It should be noted that considering that directly clustering the geological remote sensing image may be greatly affected by the resolution of the remote sensing image, it is necessary to judge and analyze the geological height information. That is, if the height data of the pixel points at a certain position in the geological remote sensing image of the real-time stage deviates greatly from that in the geological remote sensing image of the historical acquisition stage, it indicates that the pixel points at that position may have large stress deformation, and thus tend to have landslide hazard characteristics. Moreover, the stress deformation characteristics of the pixel points in the same landslide hazard area should have a certain degree of aggregation. Therefore, density clustering is performed on the geological position offset degree reflecting the stress deformation characteristics of the pixel points to realize the division of suspected landslide areas. Since the soil quality in the landslide hazard area collapses and renovates more frequently than in the normal area, and the renovated soil has a brighter gray-scale appearance, the surface geology differentiation degree of each suspected landslide area is further obtained by combining the gray-scale performance.

[0051] Specifically, denote the geological remote sensing image of the last acquisition stage as the geological remote sensing image of the real-time stage.

[0052] Preferably, in some implementation manners of the embodiment of the present invention, if the height data of the pixel points at a certain position in the geological remote sensing image of the real-time stage deviates greatly from that in the geological remote sensing image of the historical acquisition stage, it indicates that the pixel points at that position may have large stress deformation, and thus tend to have landslide hazard characteristics. The specific method for obtaining the geological position offset degree of each pixel point in the geological remote sensing image of the real-time stage according to the difference in height data of each pixel point in the geological remote sensing image of the real-time stage and its corresponding pixel point in the geological remote sensing images of other acquisition stages is as follows:

[0053] For the th pixel point in the geological remote sensing image of the real-time stage, and any one acquisition stage's geological remote sensing image other than the geological remote sensing image of the real-time stage, subtract the height data of the th pixel point in the geological remote sensing image of the real-time stage from the height data of the The absolute value of the difference between the height data of a pixel in the geological remote sensing image at any one acquisition stage is denoted as the height difference value of the pixel in the geological remote sensing image at any one acquisition stage; the mean value of the height difference values of the pixel in the geological remote sensing images at all acquisition stages except the real-time stage is used as the geological position offset degree of the pixel in the geological remote sensing image at the real-time stage; the height difference value of the pixel in the geological remote sensing image at any one acquisition stage; the mean value of the height difference values of the pixel in the geological remote sensing images at all acquisition stages except the real-time stage is used as the geological position offset degree of the pixel in the geological remote sensing image at the real-time stage; the mean value of the height difference values of the pixel in the geological remote sensing images at all acquisition stages except the real-time stage is used as the geological position offset degree of the pixel in the geological remote sensing image at the real-time stage; the geological position offset degree of the pixel in the geological remote sensing image at the real-time stage;

[0054] The specific formula is:

[0055]

[0056] In the formula, represents the geological position offset degree of the pixel in the geological remote sensing image at the real-time stage; the geological position offset degree of the pixel in the geological remote sensing image at the real-time stage; represents the number of geological remote sensing images at all acquisition stages except the real-time stage; represents the height data of the pixel in the geological remote sensing image at the real-time stage; represents the height data of the pixel in the geological remote sensing image at the th acquisition stage; represents taking the absolute value.

[0057] It should be noted that if the difference between the height data of the pixel at a certain position in the geological remote sensing image at the real-time stage and the height data of the pixel at the same position in the geological remote sensing images at other acquisition stages is larger, it indicates that the geological position offset at this position is more significant, and it indicates that the geology at this position is more likely to undergo large deformation.

[0058] Preferably, in some implementation manners of the embodiments of the present invention, since the stress and deformation characteristics of the pixels in the same landslide hazard area should have a certain aggregation, the geological position offset degree reflecting the stress and deformation characteristics of the pixels can be density-clustered to complete the division of the suspected landslide area in the geological remote sensing image at the real-time stage; the specific method for clustering the geological remote sensing image at the real-time stage according to the geological position offset degree of the pixels to obtain several suspected landslide areas in the geological remote sensing image at the real-time stage is as follows:

[0059] Input the geological position offset degrees of all pixels in the geological remote sensing image at the real-time stage into the DBSCAN density clustering algorithm to cluster all pixels in the geological remote sensing image at the real-time stage to obtain several clustering clusters; in the geological remote sensing image at the real-time stage, the area formed by all pixels in each clustering cluster is denoted as a suspected landslide area.

[0060] Among them, the DBSCAN density clustering algorithm is a prior art, and will not be elaborated here in this embodiment.

[0061] Preferably, in some implementation manners of the embodiment of the present invention, since the surface soil renovation in the landslide hazard area is more frequent than that in the normal area, the landslide hazard area has a bright white gray scale performance with obvious distinguishability in the geological remote sensing image; then, according to the gray scale distribution of the pixel points in each suspected landslide area, the specific method for obtaining the geological whitening degree of each suspected landslide area is as follows:

[0062] For the th suspected landslide area in the geological remote sensing image in the real-time stage, the difference between the mean value of the gray scale values of all the pixel points in the th suspected landslide area and the minimum value of the gray scale values of all the pixel points in the th suspected landslide area is denoted as the first difference; the difference between the maximum value of the gray scale values of all the pixel points in the th suspected landslide area and the minimum value of the gray scale values of all the pixel points in the th suspected landslide area is denoted as the first extreme value; the ratio of the first difference to the first extreme value is used as the geological whitening degree of the th suspected landslide area;

[0063] The specific formula is:

[0064]

[0065] In the formula, represents the geological whitening degree of the th suspected landslide area; represents the mean value of the gray scale values of all the pixel points in the th suspected landslide area; represents the minimum value of the gray scale values of all the pixel points in the th suspected landslide area; represents the maximum value of the gray scale values of all the pixel points in the th suspected landslide area.

[0066] Preferably, in some implementation manners of the embodiment of the present invention, due to the frequent soil renovation, the internal structure of the landslide area will be more chaotic, that is, it shows a more uneven pixel gray scale performance; then, according to the geological whitening degree, the geological position deviation degree, and the gray scale difference of the pixel points in each suspected landslide area, the specific method for obtaining the surface quality distinguishability of each suspected landslide area is as follows:

[0067] The gray scale value of the th pixel point in the th suspected landslide area is compared with the The absolute value of the difference between the means of the gray values of all pixel points in a suspected landslide area is denoted as the gray value difference of the th pixel point; the mean of the gray value differences of all pixel points in the th suspected landslide area is denoted as the gray scale difference of the th suspected landslide area; the normalized value of the product of the gray scale difference of the th suspected landslide area, the degree of geological whitening of the th suspected landslide area, and the mean of the geological position offsets of all pixel points in the th suspected landslide area is used as the surface quality differentiation degree of the th suspected landslide area;

[0068] The specific formula is:

[0069]

[0070] In the formula, represents the surface quality differentiation degree of the th suspected landslide area; represents the degree of geological whitening of the th suspected landslide area; represents the mean of the geological position offsets of all pixel points in the th suspected landslide area; represents the number of all pixel points in the th suspected landslide area; represents the gray value of the th pixel point in the th suspected landslide area; represents the mean of the gray values of all pixel points in the th suspected landslide area; represents taking the absolute value; represents the linear normalization function.

[0071] Specifically, map the positions of each suspected landslide area in the geological remote sensing image in the real-time stage to the geological remote sensing image in other sampling stages, and obtain the surface quality differentiation degree of the suspected landslide area in the geological remote sensing image in other sampling stages through the above method.

[0072] Thus, the surface quality differentiation degree of each suspected landslide area in the geological remote sensing image in each sampling stage is obtained through the above method.

[0073] Step S003: Obtain the landslide surface quality presentation degree of each suspected landslide area according to the surface quality differentiation degree in the geological remote sensing images of each suspected landslide area at different sampling stages; obtain the landslide deformation feature compliance degree of each suspected landslide area according to the distribution of the height data of the pixel points in the geological remote sensing image of the real-time stage and the geological remote sensing images of its adjacent sampling stages of each suspected landslide area; obtain the landslide hazard tendency index of each suspected landslide area according to the landslide deformation feature compliance degree and the landslide surface quality presentation degree.

[0074] It should be noted that considering that the geological surface soil in the landslide hazard area is frequently renovated, while the normal area is relatively stable, the landslide surface quality presentation degree of the suspected landslide area is obtained according to the updated performance of the surface quality differentiation degree at different acquisition stages; at the same time, the more obvious the depression feature in the upper part and the convex feature in the lower part within the suspected landslide area, the more uneven the internal stress deformation in the suspected landslide area, and the higher the landslide hazard in the suspected landslide area. Therefore, the landslide hazard reflection degree of each suspected landslide area is further obtained by combining the internal stress deformation law in the suspected landslide area.

[0075] Preferably, in some implementation manners of the embodiment of the present invention, the specific method for obtaining the landslide surface quality presentation degree of each suspected landslide area according to the surface quality differentiation degree in the geological remote sensing images of each suspected landslide area at different sampling stages is as follows:

[0076] Construct a two-dimensional coordinate system with the serial number of the sampling stage as the abscissa and the surface quality differentiation degree as the ordinate, input the surface quality differentiation degrees of the th suspected landslide area in the geological remote sensing images of all sampling stages into the two-dimensional coordinate system, and use the least square method for curve fitting to obtain the surface quality differentiation change curve of the th suspected landslide area;

[0077] Use the STL trend item decomposition method to decompose the surface quality differentiation change curve of the th suspected landslide area to obtain the trend item curve of the th suspected landslide area;

[0078] Take the mean value of the slopes of all data points on the trend item curve of the th suspected landslide area as the trend item slope mean value of the th suspected landslide area; take the ratio between the trend item slope mean value of the th suspected landslide area and the maximum value of the trend item slope mean values of all suspected landslide areas as the trend item slope ratio; take the product of the trend item slope ratio and the surface quality differentiation degree of the th suspected landslide area in the geological remote sensing image of the real-time stage as the The landslide surface quality presentation degree of a suspected landslide area;

[0079] The specific formula is:

[0080]

[0081] In the formula, represents the landslide surface quality presentation degree of the th suspected landslide area; represents the mean value of the slopes of all data points on the trend item curve of the th suspected landslide area; represents the maximum value of the mean values of the trend item slopes of all suspected landslide areas; represents the surface quality differentiation degree of the th suspected landslide area in the geological remote sensing image in the real-time stage.

[0082] It should be noted that the more the corresponding trend item curve of the suspected landslide area shows an upward trend, and at the same time, the greater the surface quality differentiation degree of the suspected landslide area in the geological remote sensing image in the real-time stage, the higher the landslide hidden danger of the suspected landslide area; The least squares method and the STL trend item decomposition method are existing technologies, and will not be elaborated here in this embodiment.

[0083] Preferably, in some implementation manners of the embodiment of the present invention, since there is often a deformation demarcation line in the landslide hidden danger area, showing a concave feature above the deformation demarcation line and a convex feature below the deformation demarcation line; According to the distribution of the height data of the pixel points in the geological remote sensing image of each suspected landslide area in the real-time stage and the geological remote sensing image of its adjacent sampling stage, the specific method for obtaining the landslide deformation feature compliance degree of each suspected landslide area is:

[0084] Record the geological remote sensing image of the previous acquisition stage of the geological remote sensing image in the real-time stage as the geological remote sensing image in the comparison stage;

[0085] In the th suspected landslide area in the geological remote sensing image in the real-time stage, record the pixel point with the maximum height data as the height maximum value pixel point of the th suspected landslide area; Record the pixel point with the minimum height data as the height minimum value pixel point of the th suspected landslide area; Record the straight line connection between the height maximum value pixel point of the th suspected landslide area and the height minimum value pixel point of the th suspected landslide area as the soil flow direction straight line of the th suspected landslide area;

[0086] For the th suspected landslide area, on the soil flow direction straight line of the pixel, and using the rd pixel, and perpendicular to the rd straight line of the soil flow direction in the suspected landslide area, divide the th suspected landslide area into an upper area and a lower area;

[0087] Take the average value of the height data of all pixels in the lower area of the th suspected landslide area in the geological remote sensing image in the real-time stage as the first height average value; take the average value of the height data of all pixels in the lower area of the

[0088] th suspected landslide area in the geological remote sensing image in the comparison stage as the second height average value; take the absolute value of the difference between the first height average value and the second height average value as the convexity degree of the lower area of the th pixel; Take the

[0089] average value of the height data of all pixels in the upper area of the th suspected landslide area in the geological remote sensing image in the real-time stage as the third height average value; take the average value of the height data of all pixels in the upper area of the th suspected landslide area in the geological remote sensing image in the comparison stage as the fourth height average value; take the absolute value of the difference between the third height average value and the fourth height average value as the

[0090] concavity degree of the upper area of the th pixel; Take the ratio of the concavity degree of the upper area of the th pixel to the convexity degree of the lower area of the th pixel as the deformation depression and bulge ratio of the

[0091] th pixel;

[0092]

[0093] In the formula, represents the degree of conformity of the landslide deformation characteristics of the th suspected landslide area; represents the maximum value of the height data of all pixel points in the th suspected landslide area; represents the height data of the target pixel point in the th suspected landslide area; represents the deformation subsidence ratio of the target pixel point in the th suspected landslide area.

[0094] It should be noted that as time goes by, for the landslide hazard area, the internal sunken soil part will gradually accumulate towards the convex part, resulting in the upward movement of the deformation demarcation line. Therefore, the more the deformation demarcation line tends to the lowest point of height, the smaller the landslide hazard in the suspected landslide area.

[0095] Preferably, in some implementation manners of the embodiments of the present invention, the greater the degree of appearance of the landslide surface quality in the suspected landslide area and the higher the degree of conformity of the landslide deformation characteristics, the greater the probability of reflecting the landslide hazard in the suspected landslide area; the specific method for obtaining the landslide hazard tendency index of each suspected landslide area according to the degree of conformity of the landslide deformation characteristics and the degree of appearance of the landslide surface quality is as follows:

[0096] Multiply the degree of conformity of the landslide deformation characteristics of the th suspected landslide area by the degree of appearance of the landslide surface quality of the th suspected landslide area as the degree of reflection of the landslide hazard of the th suspected landslide area;

[0097] Normalize the ratio between the degree of reflection of the landslide hazard of the th suspected landslide area and the average value of the degrees of reflection of the landslide hazards of all suspected landslide areas as the landslide hazard tendency index of the th suspected landslide area.

[0098] Thus, the landslide hazard tendency index of each suspected landslide area is obtained through the above method.

[0099] Step S004: Explore the landslide hazard areas in the geological remote sensing image in the real-time stage based on the landslide hazard tendency index.

[0100] Preferably, in some implementation manners of the embodiments of the present invention, the specific method for exploring the landslide hazard areas in the geological remote sensing image in the real-time stage based on the landslide hazard tendency index is as follows:

[0101] Preset two threshold parameters and , where in this embodiment, and will be described by taking... as an example. This embodiment is not specifically limited, where and are determined according to the specific implementation situation;

[0102] For any suspected landslide area, if the landslide hazard tendency index of the any suspected landslide area is less than the threshold parameter , the any suspected landslide area is recorded as a low landslide risk area; if the landslide hazard tendency index of the any suspected landslide area is greater than the threshold parameter , the any suspected landslide area is recorded as a high landslide risk area.

[0103] Please refer to Figure 2 , which shows the characteristic relationship flowchart of the geological exploration method of the integrated remote sensing image processing technology;

[0104] So far, this embodiment is completed.

[0105] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A geological exploration method using integrated remote sensing image processing technology, characterized in that, The method includes the following steps: Obtain geological remote sensing images of several acquisition stages, and the height data of each pixel point in the geological remote sensing images of each acquisition stage; According to the difference situation of the height data of each pixel point in the geological remote sensing image of the real-time stage and its corresponding pixel points in the geological remote sensing images of other acquisition stages, obtain the geological position offset degree of each pixel point in the geological remote sensing image of the real-time stage; cluster the geological remote sensing image of the real-time stage according to the geological position offset degree of the pixel points to obtain several suspected landslide areas in the geological remote sensing image of the real-time stage; according to the gray-scale distribution of the pixel points in each suspected landslide area, obtain the geological whitening degree of each suspected landslide area; according to the geological whitening degree, the geological position offset degree, and the gray-scale difference situation of the pixel points in each suspected landslide area, obtain the surface geology differentiation degree of each suspected landslide area; According to the surface geology differentiation degree of each suspected landslide area in the geological remote sensing images of different sampling stages, obtain the landslide surface geology presentation degree of each suspected landslide area; according to the distribution situation of the height data of the pixel points in the geological remote sensing image of each suspected landslide area in the real-time stage and its adjacent sampling stage, obtain the landslide deformation feature compliance degree of each suspected landslide area; according to the landslide deformation feature compliance degree and the landslide surface geology presentation degree, obtain the landslide hazard tendency index of each suspected landslide area; Based on the landslide hazard tendency index, explore the landslide hazard areas in the geological remote sensing image of the real-time stage; The specific method for obtaining the geological whitening degree of each suspected landslide area includes: For the j-th suspected landslide area in the geological remote sensing image of the real-time stage, record the difference between the mean value of the gray-scale values of all pixel points in the j-th suspected landslide area and the minimum value of the gray-scale values of all pixel points in the j-th suspected landslide area as the first difference; record the difference between the maximum value of the gray-scale values of all pixel points in the j-th suspected landslide area and the minimum value of the gray-scale values of all pixel points in the j-th suspected landslide area as the first extreme value; take the ratio of the first difference to the first extreme value as the geological whitening degree of the j-th suspected landslide area; Record the absolute value of the difference between the gray-scale value of the y-th pixel point in the j-th suspected landslide area and the mean value of the gray-scale values of all pixel points in the j-th suspected landslide area as the gray-scale difference value of the y-th pixel point; record the mean value of the gray-scale difference values of all pixel points in the j-th suspected landslide area as the gray-scale difference of the j-th suspected landslide area; take the normalized value of the product of the gray-scale difference of the j-th suspected landslide area, the geological whitening degree of the j-th suspected landslide area, and the mean value of the geological position offset degrees of all pixel points in the j-th suspected landslide area as the surface geology differentiation degree of the j-th suspected landslide area.

2. The geological exploration method of the integrated remote sensing image processing technology according to claim 1, characterized in that, The specific method for obtaining the geological position offset degree of each pixel point in the geological remote sensing image of the real-time stage includes: For the th pixel point in the geological remote sensing image of the real-time stage, and for any geological remote sensing image of a collection stage other than the geological remote sensing image of the real-time stage, the absolute value of the difference between the height data of the th pixel point in the geological remote sensing image of the real-time stage and the height data of the th pixel point in the geological remote sensing image of the said any collection stage is denoted as the height difference value of the th pixel point in the geological remote sensing image of the said any collection stage; the mean value of the height difference values of the th pixel point in all geological remote sensing images of collection stages other than the geological remote sensing image of the real-time stage is taken as the geological position deviation degree of the th pixel point in the geological remote sensing image of the real-time stage.

3. The geological exploration method of the integrated remote sensing image processing technology according to claim 1, characterized in that, The specific method for obtaining several suspected landslide areas in the geological remote sensing image of the real-time stage includes: Input the geological position offset degrees of all pixel points in the geological remote sensing image in the real-time stage into the DBSCAN density clustering algorithm to cluster all pixel points in the geological remote sensing image in the real-time stage, and obtain several clusters; in the geological remote sensing image in the real-time stage, mark the area formed by all pixel points in each cluster as a suspected landslide area.

4. The geological exploration method of the integrated remote sensing image processing technology according to claim 1, characterized in that, The specific method for obtaining the landslide surface quality presentation degree of each suspected landslide area includes: Taking the serial number of the sampling stage as the abscissa and the surface quality differentiation as the ordinate, a two-dimensional coordinate system is constructed. The surface quality differentiations of the th suspected landslide area in the geological remote sensing images at all sampling stages are input into the two-dimensional coordinate system, and curve fitting is performed using the least squares method to obtain the surface quality differentiation change curve of the th suspected landslide area; Use the STL trend item decomposition method to decompose the surface mass differentiation change curve of the th suspected landslide area to obtain the trend item curve of the th suspected landslide area; Denote the mean of the slopes of all data points on the trend item curve of the th suspected landslide area as the mean trend item slope of the th suspected landslide area; The ratio between the mean slope of the trend term of the th suspected landslide area and the maximum value of the mean slope of the trend terms of all suspected landslide areas is denoted as the trend term slope ratio; Multiply the slope ratio of the trend term by the surface quality differentiation degree of the th suspected landslide area in the geological remote sensing image of the real-time stage, and use the product as the landslide surface quality presentation degree of the th suspected landslide area.

5. The geological exploration method of the integrated remote sensing image processing technology according to claim 1, characterized in that, The specific method for obtaining the landslide deformation feature compliance degree of each suspected landslide area includes: Denote the geological remote sensing image in the previous acquisition stage of the geological remote sensing image in the real-time stage as the geological remote sensing image in the comparison stage; According to the distribution difference of the height data of the pixel points in the geological remote sensing images of the th suspected landslide area in the real-time stage and the geological remote sensing images in the comparison stage, obtain the deformation subsidence ratio of the target pixel points of the th suspected landslide area; The difference between the maximum value of the height data of all pixel points in the th suspected landslide area and the height data of the target pixel point in the th suspected landslide area is denoted as the deformation height difference. The product of the deformation subsidence ratio of the target pixel point in the th suspected landslide area and the reciprocal of the deformation height difference is taken as the landslide deformation characteristic compliance degree of the th suspected landslide area.

6. The geological exploration method of the integrated remote sensing image processing technology according to claim 5, characterized in that The method for obtaining the deformation subsidence ratio of the target pixel points of the th suspected landslide area specifically includes: In the th suspected landslide area in the geological remote sensing image at the real-time stage, the pixel point with the maximum height data is recorded as the height maximum pixel point of the th suspected landslide area; the pixel point with the minimum height data is recorded as the height minimum pixel point of the th suspected landslide area; the straight-line connection between the height maximum pixel point of the th suspected landslide area and the height minimum pixel point of the th suspected landslide area is recorded as the soil flow direction straight line of the th suspected landslide area; For the th pixel point on the straight line of the soil flow direction in the th suspected landslide area, use the th pixel point and be perpendicular to the straight line of the soil flow direction in the th suspected landslide area to divide the th suspected landslide area into an upper area and a lower area; Denote the mean value of the height data of all pixel points in the lower area of the th suspected landslide area in the geological remote sensing image in the real-time stage as the first height mean value; Denote the mean value of the height data of all pixel points in the lower area of the th suspected landslide area in the geological remote sensing image in the comparison stage as the second height mean value; Denote the absolute value of the difference between the first height mean value and the second height mean value as the convexity degree of the lower area of the th pixel point; Denote the mean value of the height data of all pixel points in the upper region of the th suspected landslide area in the geological remote sensing image in the real-time stage as the third height mean value; denote the mean value of the height data of all pixel points in the upper region of the th suspected landslide area in the geological remote sensing image in the comparison stage as the fourth height mean value; denote the absolute value of the difference between the third height mean value and the fourth height mean value as the degree of depression of the upper region of the th pixel point; Take the ratio of the degree of depression in the upper region of the th pixel to the degree of convexity in the lower region of the th pixel as the deformation depression-convexity ratio of the th pixel; Among all the pixel points on the straight line of the soil flow direction in the th suspected landslide area, the pixel point corresponding to the maximum value of the deformation subsidence ratio is recorded as the target pixel point of the th suspected landslide area.

7. The geological exploration method of the integrated remote sensing image processing technology according to claim 1, characterized in that, The specific method for obtaining the landslide hazard tendency index of each suspected landslide area according to the landslide deformation feature compliance degree and the landslide surface quality presentation degree includes: Multiply the degree of conformity of the landslide deformation characteristics of the th suspected landslide area by the degree of surface material manifestation of the landslide in the th suspected landslide area, and use the result as the degree of landslide hazard reflection of the th suspected landslide area; The normalization value of the ratio between the landslide hazard reflection degree of the th suspected landslide area and the average value of the landslide hazard reflection degrees of all suspected landslide areas is used as the landslide hazard tendency index of the th suspected landslide area.

8. The geological exploration method of the integrated remote sensing image processing technology according to claim 1, characterized in that, The specific method for prospecting the landslide hazard area in the geological remote sensing image in the real-time stage based on the landslide hazard tendency index includes: Preset two threshold parameters and , for any suspected landslide area, if the landslide hazard tendency index of the any suspected landslide area is less than the threshold parameter , mark the any suspected landslide area as a low landslide risk area; If the landslide hazard tendency index of any of the suspected landslide areas is greater than the threshold parameter , mark any of the suspected landslide areas as a high landslide risk area.

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