A remote sensing image processing method and system integrating DEM elevation information

Abstract

The present application relates to the field of image processing technology, and specifically to a remote sensing image processing method and system integrating DEM elevation information, the method comprising: obtaining DEM data and remote sensing images of a target area and matching them, obtaining a vertex grid with the center point of the grid in the DEM data as the vertex; obtaining internal remote sensing pixel points; classifying the internal remote sensing pixel points in the vertex grid, and judging whether the vertex grid is a vertex grid to be distinguished according to the quantity distribution characteristics; obtaining the concave-convex distinction value of each vertex grid to be distinguished according to the grayscale distribution of the pixel points in the neighborhood range of each internal remote sensing pixel point in the vertex grid to be distinguished, and obtaining the category to which each vertex grid to be distinguished belongs; obtaining elevation information; and correcting the elevation information of the pixel points of the remote sensing image in combination with the position of the vertex in each vertex grid to be distinguished from each pixel point. The present application aims to correct the elevation information of the pixel points according to different terrain features, so that the elevation information is more accurate.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a remote sensing image processing method and system integrating DEM elevation information. Background Art

[0002] Digital Elevation Model (DEM) data provides surface elevation information, which represents the ups and downs and morphology of the surface in digital form. Remote sensing images provide surface planar image information, including the shape, color, texture, etc. of the objects. By fusing these two types of information, an image containing both planar image information and elevation information can be generated, which can reflect geographic information more comprehensively and realistically.

[0003] The resolution of the fused remote sensing image is limited by the resolution of the DEM data and the remote sensing image. If the resolution of the DEM data is low or the resolution of the remote sensing image is low, the fused image may lack accuracy in elevation information. When interpreting the geological environment of a mine, the resolution of the DEM data is usually low, resulting in a grid in the actual DEM data representing the height information of a small area in the remote sensing image. The traditional fused image directly fills in the missing elevation data through interpolation, which results in large errors in the elevation, slope, and aspect information in the fused topographic map. Summary of the invention

[0004] In view of the above, it is necessary to provide a remote sensing image processing method and system integrating DEM elevation information to solve the above problems.

[0005] The first aspect of the present application provides a remote sensing image processing method integrating DEM elevation information, the method comprising:

[0006] Obtain DEM data and remote sensing images of the target area;

[0007] The DEM data and remote sensing images of the target area are matched, and each vertex grid is obtained by taking the center point of the grid in the DEM data as the vertex; the internal remote sensing pixel points are obtained according to the positional relationship between the pixel center point in the remote sensing image and the vertex grid; the internal remote sensing pixel points in each vertex grid are classified based on the grayscale value, and the vertex grid is judged whether it is a vertex grid to be distinguished according to the quantitative distribution characteristics of the classified internal remote sensing pixel points in the vertex grid;

[0008] According to the grayscale distribution of the pixel points in the neighborhood of each internal remote sensing pixel point in the vertex grid to be distinguished, the concave-convex distinction value of each vertex grid to be distinguished is obtained, and the category to which each vertex grid to be distinguished belongs is obtained;

[0009] The elevation information of each pixel is obtained; the elevation information of each pixel of the remote sensing image is corrected according to the category of each vertex grid to be distinguished and the position of the vertex in each vertex grid to be distinguished from each pixel.

[0010] The step of obtaining each vertex grid is as follows:

[0011] The adjacent grid center points in the same row and the adjacent grid center points in the same column are connected by straight lines to obtain a vertex grid.

[0012] The process of obtaining the internal remote sensing pixel points is as follows:

[0013] The remote sensing image pixel points whose pixel centers are within the vertex grid are regarded as the internal remote sensing pixel points of the vertex grid;

[0014] If the center of a remote sensing image pixel belongs to an edge pixel of the vertex grid, the corresponding pixel is recorded as an internal remote sensing pixel.

[0015] The step of determining whether a vertex grid is a vertex grid to be distinguished is as follows:

[0016] Obtain the average gray value of the internal remote sensing pixel points of each vertex grid, which is recorded as the first segmentation threshold;

[0017] The internal remote sensing pixel points in the vertex grid that are greater than the first segmentation threshold are regarded as the first type of pixel points;

[0018] According to the quantity distribution characteristics of the first type of pixel points in each vertex grid, the concave-convex discrimination value of each vertex grid is obtained;

[0019] The concave-convex discrimination values ​​of all vertex grids are subjected to a threshold segmentation algorithm to obtain a second segmentation threshold; and the vertex grids whose concave-convex discrimination values ​​are greater than the second segmentation threshold are used as vertex grids to be distinguished.

[0020] The concave-convex discrimination value of each vertex grid is obtained as follows:

[0021] The difference between the number of first-category pixels in the left half and the right half of the vertex grid is recorded as the first difference; the difference between the number of first-category pixels in the upper half and the lower half of the vertex grid is recorded as the second difference;

[0022] The first difference and the second difference of each vertex grid are combined to obtain the concave-convex discrimination value of each vertex grid, wherein the concave-convex discrimination value is positively correlated with the first difference and the second difference.

[0023] The step of obtaining the concave-convex distinction value of each vertex grid to be distinguished is specifically as follows:

[0024] For each vertex grid to be distinguished, a neighborhood window is preset with each internal remote sensing pixel as the center; the information entropy of the grayscale values ​​of all pixels in the neighborhood window of each internal remote sensing pixel is obtained; the grayscale histogram of the vertex grid to be distinguished is obtained, and the variance of the number of pixels corresponding to each grayscale level is calculated, and the ratio of the information entropy to the variance is used as the concave-convex distinction value of the vertex grid to be distinguished.

[0025] The step of obtaining the category of each vertex grid to be distinguished is as follows:

[0026] A clustering algorithm is used to divide all vertex grids to be distinguished into two categories based on the concave-convex distinguishing values; the average concave-convex distinguishing values ​​of all vertex grids to be distinguished in each category are calculated, and the category with the largest average value is classified as the convex area; and the remaining category is classified as the concave area.

[0027] The process of correcting the elevation information of each pixel point of the remote sensing image is specifically as follows:

[0028] According to the distance distribution of each pixel point from the four vertices in the vertex grid where it is located, combined with the elevation information of each pixel point, the correction factor of each pixel point is obtained;

[0029] When the vertex grid where the pixel point is located is a convex area, the sum of the elevation information of the corresponding pixel point and the correction factor is used as the corrected elevation information of the pixel point;

[0030] When the vertex grid where the pixel point is located is a concave area, the difference between the elevation information of the corresponding pixel point and the correction factor is used as the corrected elevation information of the pixel point;

[0031] Otherwise, the elevation information of the corresponding pixel remains unchanged.

[0032] The correction factor for each pixel is obtained as follows:

[0033] The ratio of the minimum distance to the maximum distance between each pixel point and the four vertices of the vertex grid where the pixel point is located is calculated; the product of the elevation information of each pixel point and the ratio and a preset value is calculated as a correction factor.

[0034] In the second aspect, an embodiment of the present application also provides a remote sensing image processing system that integrates DEM elevation information, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0035] In the above scheme, the DEM data and remote sensing images of the target area are obtained and matched. Since each grid in the DEM data corresponds to multiple grids in the remote sensing image, in the process of fusing the DEM data and the remote sensing image, the grid center point in the DEM data is used as the vertex to obtain each vertex grid, which is helpful for the subsequent analysis of the concave and convex features of the terrain where the pixel point in the remote sensing image is located, and based on this, the elevation information corresponding to the pixel point is corrected; the internal remote sensing pixel point is obtained according to the positional relationship between the pixel center point and the vertex grid, which is helpful for distinguishing the vertex grid where the pixel point is located, making the subsequent analysis more accurate; the internal remote sensing pixel points in each vertex grid are classified based on the grayscale value, and the classification is carried out according to the classification. The number distribution characteristics of the internal remote sensing pixel points after classification in the vertex grid are used to determine whether the vertex grid is the vertex grid to be distinguished; the vertex grids with concave-convex features are screened out to further narrow the range of pixels that need to correct the elevation information; according to the grayscale distribution of pixels in the neighborhood range of each internal remote sensing pixel point in the vertex grid to be distinguished, the concave-convex distinction value of each vertex grid to be distinguished is obtained, and the category to which each vertex grid to be distinguished belongs is obtained. The beneficial effect is that the vertex grids with concave-convex features are further divided, which is helpful for the subsequent correction of the elevation information of the pixel points according to different terrain features, so that the elevation information of the corrected remote sensing image can more accurately reflect the elevation, slope aspect and slope information of the open-pit mine. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of a remote sensing image processing method integrating DEM elevation information provided in one embodiment of the present application;

[0037] Figure 2 A local schematic diagram of the registration of DEM data and remote sensing images provided in one embodiment of the present application. DETAILED DESCRIPTION

[0038] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example" and the like are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "or", "for example" and the like is intended to present related concepts in a concrete manner.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0040] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The method disclosed in the embodiments of the present application or the method shown in the flow chart includes one or more steps for implementing the method. Without departing from the scope of protection of the present application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0041] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0042] The following is a detailed description of a remote sensing image processing method and system for integrating DEM elevation information provided by the present application with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flowchart of a remote sensing image processing method for integrating DEM elevation information provided by an embodiment of the present application, the method comprising the following steps:

[0044] The first step: obtain DEM data and remote sensing images of the target area.

[0045] The DEM data and remote sensing images of a certain open-pit mine distribution area are obtained by using the Geographic Information System (GIS). In this embodiment, the accuracy of the DEM data is set to 30 meters. The accuracy of 30 meters means that each grid in the DEM data corresponds to an area of ​​30 meters × 30 meters on the ground. The implementer can set it according to the actual situation. The median filtering algorithm is used to remove the noise in the remote sensing image. The median filtering is a well-known technology and the specific process will not be repeated.

[0046] The second step: match the DEM data and remote sensing image of the target area, take the grid center point in the DEM data as the vertex, and obtain each vertex grid; obtain the internal remote sensing pixel point based on the positional relationship between the pixel center point in the remote sensing image and the vertex grid; classify the internal remote sensing pixel points in each vertex grid based on the grayscale value, and determine whether the vertex grid is a vertex grid to be distinguished based on the quantitative distribution characteristics of the classified internal remote sensing pixel points in the vertex grid.

[0047] DEM data is a type of raster data model, consisting of a set of ordered numerical arrays. The difference between it and the raster representation of an image is that in DEM data, the value of each grid cell represents the ground elevation of the center point of the cell, and the elevation data between adjacent grid center points is calculated by interpolation to estimate the height difference between the center points of each grid cell.

[0048] First, the DEM data and the remote sensing image are scaled to the same scale, and then the DEM data and the remote sensing image are used as the input of the illumination model to realize the registration of the DEM data and the remote sensing image. The illumination model is a well-known technology, and the specific process is not repeated here. Figure 2 As shown, Figure 2 In the DEM data, due to the low accuracy, each grid in the DEM data will correspond to multiple grids in the remote sensing image. Figure 2 In the solid line part, the grid with thick lines represents the grid of DEM data, and the grid with thin lines represents the pixel point of remote sensing image; the center point of each grid of DEM data is recorded as the center point of DEM grid. The traditional fusion method is to calculate the height information of each pixel point in remote sensing image by bilinear interpolation method based on the elevation information of the center point of each grid in DEM data, so as to achieve the purpose of fusion. However, this method is only applicable to the case where the slope of open-pit mine changes gently and smoothly. If there is a height change in the form of convex or concave between the center points of adjacent grids in DEM data, the elevation information of each pixel point in remote sensing image cannot be accurately calculated by bilinear interpolation method.

[0049] In order to make the elevation information of DEM data more accurately describe the elevation information of each pixel in the remote sensing image during the interpolation process, so as to accurately analyze the slope, height, slope direction and other information of the mine, it is necessary to identify the areas containing convex or concave areas in the remote sensing image based on the color characteristics and texture characteristics in the remote sensing image. When using the bilinear interpolation method to calculate the elevation information of each pixel in the remote sensing image, the elevation information of each pixel is calculated based on the elevation information of the four DEM grid center points closest to each pixel in Euclidean distance. Connect the center points of each row and column of the DEM grid to obtain a new grid, recorded as the vertex grid, such as Figure 2 As shown by the dotted line in , the remote sensing image pixel whose pixel center is located in the vertex grid is regarded as the internal remote sensing pixel of the vertex grid. If the remote sensing image pixel center belongs to the edge pixel of the vertex grid, the corresponding pixel is recorded as the internal remote sensing pixel.

[0050] It should be noted that, in order to facilitate subsequent analysis based on the vertex grids where the pixel points are located, in this embodiment, if the center of the pixel point falls on the common edge of two adjacent vertex grids at the same time, if the common edge is in the vertical direction, then this embodiment will classify the corresponding pixel point as the vertex grid to the left of the common edge; if the common edge is in the horizontal direction, then this embodiment will classify the corresponding pixel point as the vertex grid above the common edge.

[0051] Since it is only necessary to determine whether the open-pit mine area corresponding to the internal remote sensing pixel point of the vertex grid is a convex area or a concave area, the elevation information of each internal remote sensing pixel point in the vertex grid can be corrected. If the open-pit mine in the vertex grid is a convex area, then this area generally corresponds to the mine ridge or the top of the mine slope, and its height is generally distributed in an uplift, showing a convex function, and the height data obtained by bilinear interpolation is a linear change. Therefore, when calculating the elevation information of the remote sensing image pixel points in the vertex grid, the elevation data needs to be appropriately enlarged. If the open-pit mine in the vertex grid is a concave area, then this area generally corresponds to the ditch in the mine or the bottom of the mine slope, and its height is generally distributed in a concave distribution, showing a concave function, and the height data obtained by bilinear interpolation is a linear change. Therefore, when calculating the elevation information of the remote sensing image pixel points in the vertex grid, the elevation data needs to be appropriately reduced.

[0052] Regardless of whether it is a raised area or a sunken area, under the influence of light, the side facing the sun will be brighter, and the side facing away from the sun will be darker. Therefore, when an area appears bright on one side and dark on the other, the area is likely to be a raised area or a sunken area, and the grayscale value corresponding to the area will be larger in part and smaller in part.

[0053] Based on this, the internal remote sensing pixels in each vertex grid are classified, and the concave-convex discrimination value of each vertex grid is obtained according to the quantity distribution characteristics of the classified internal remote sensing pixels in the vertex grid: the average gray value of the internal remote sensing pixels of each vertex grid is obtained, which is recorded as the first segmentation threshold; the internal remote sensing pixels in the vertex grid that are greater than the first segmentation threshold are taken as the first type of pixels; the difference in the number of the first type of pixels in the left half and the right half of the vertex grid is recorded as the first difference; the difference in the number of the first type of pixels in the upper half and the lower half of the vertex grid is recorded as the second difference; the concave-convex discrimination value of each vertex grid is obtained by combining the first difference and the second difference of each vertex grid, wherein the concave-convex discrimination value is positively correlated with the first difference and the second difference.

[0054] It should be noted that a positive correlation indicates that as one variable increases, another variable also increases, and a negative correlation indicates that as one variable increases, another variable also decreases.

[0055] In the process of obtaining the concave-convex discrimination value, the difference between the variables is measured by using the absolute value of the difference, and the sum of the first difference and the second difference is used as the concave-convex discrimination value of each vertex grid.

[0056] It should be understood that if the difference between the number of first-class pixel points in the left half of the internal remote sensing pixel points of the vertex grid and the number of first-class pixel points in the right half is greater, it means that the difference between the number of brighter pixel points in the left half and the right half is greater, and then it means that the left half and the right half of the vertex grid are bright on one side and dark on the other side, then the area where the vertex grid is located is more likely to be a convex or concave area. Similarly, if the difference between the number of first-class pixel points in the upper half of the internal remote sensing pixel points of the vertex grid and the number of first-class pixel points in the lower half is greater, the area where the vertex grid is located is more likely to be a convex or concave area.

[0057] Calculate the concave-convex discrimination values ​​of all vertex grids, use the concave-convex discrimination values ​​of all vertex grids as input of the Otsu threshold segmentation algorithm, and obtain the second segmentation threshold. The Otsu threshold segmentation algorithm is a well-known technology, and the specific process is not repeated. If the concave-convex discrimination value of the i-th vertex grid is greater than the second segmentation threshold, the i-th vertex grid is recorded as the vertex grid to be distinguished.

[0058] The third step: according to the grayscale distribution of the pixels in the neighborhood of each internal remote sensing pixel in the vertex grid to be distinguished, the concave-convex distinction value of each vertex grid to be distinguished is obtained, and the category to which each vertex grid to be distinguished belongs is obtained.

[0059] When a vertex grid to be distinguished is a convex area, the area with trees in the area will make the grayscale value distribution of remote sensing pixels more chaotic, and the sum of the information entropy of all sliding windows in the internal remote sensing pixels in the vertex grid will be larger; on the contrary, when a vertex grid is a concave area, due to the smooth texture, the grayscale value distribution of each sliding window in the area will be more consistent, and the sum of the information entropy of all sliding windows in the internal remote sensing pixels in the vertex grid will be smaller. In addition, since there are some trees or vegetation in the convex area, and the colors of trees and vegetation are different, the grayscale values ​​in the tree and vegetation area will be more diverse, while the concave area is mainly bare land, the color is relatively single, and the types of grayscale values ​​will be fewer. Therefore, when a vertex grid is a convex area, the grayscale values ​​of the internal remote sensing pixels of the vertex grid will be more dispersed in each grayscale level of the grayscale histogram; when a vertex grid is a concave area, the grayscale values ​​of the remote sensing pixels in the vertex grid will be more concentrated in a part of the grayscale level of the grayscale histogram.

[0060] Therefore, according to the grayscale distribution of pixels within the neighborhood of each internal remote sensing pixel in the vertex grid to be distinguished, the concave-convex distinction value of each vertex grid to be distinguished is obtained: for each vertex grid to be distinguished, a neighborhood window is preset with each internal remote sensing pixel as the center; the information entropy of the grayscale values ​​of all pixels within the neighborhood window of each internal remote sensing pixel is obtained; the grayscale histogram of the vertex grid to be distinguished is obtained, and the variance of the number of pixels corresponding to each grayscale level is calculated, and the ratio of the information entropy to the variance is used as the concave-convex distinction value of the vertex grid to be distinguished.

[0061] It should be noted that, in order to avoid the situation where the denominator is zero, a preset value needs to be added to the denominator, and the preset value in this embodiment is 0.1.

[0062] In the process of obtaining the concave-convex distinction value, the size of the neighborhood window is, which can be set by the implementer according to the actual situation. When the concave-convex distinction value is large, it means that the texture of the geographical area where the vertex grid to be distinguished is located is mottled, which means that the vertex grid is more likely to belong to the convex area. When the concave-convex distinction value is small, it means that the number of pixels in each gray level of the vertex grid to be distinguished is close, which means that the gray values ​​of the remote sensing pixels inside the vertex grid to be distinguished are more dispersed, then the vertex grid to be distinguished is more likely to be a convex area.

[0063] Calculate the concave-convex distinction values ​​of all vertex grids to be distinguished, and use the concave-convex distinction values ​​of all vertex grids to be distinguished as the input of the K-means algorithm. In this embodiment, k is 2, and two clusters are obtained. The K-means algorithm is a well-known technology, and the specific process is not repeated here. Calculate the average concave-convex distinction value of each cluster, and take the cluster with a larger average concave-convex distinction value as the convex area, and take the cluster with a smaller average concave-convex distinction value as the concave area.

[0064] The fourth step: obtain the elevation information of each pixel point; according to the category of each vertex grid to be distinguished and the position of the vertex in each vertex grid to be distinguished from each pixel point, correct the elevation information of each pixel point in the remote sensing image.

[0065] The elevation information of the DEM data and the resolution of the remote sensing image are used as the input of the bilinear interpolation method, and the elevation information is assigned to each pixel point of the remote sensing image to obtain the elevation information of each pixel point of the remote sensing image. The bilinear interpolation method is a well-known technology and the specific process will not be repeated here.

[0066] When the vertex grid where the i-th pixel in the remote sensing image is located belongs to the vertex grid of the convex area, the actual elevation information of the i-th pixel is greater than the elevation information calculated by the bilinear interpolation method, and the closer the i-th pixel is to the center area of ​​the vertex grid, the more likely the pixel is to be located at the top of the convexity, that is, the greater the elevation information of the pixel. When the vertex grid where the i-th pixel in the remote sensing image is located belongs to the vertex grid of the concave area, the actual elevation information of the i-th pixel is smaller than the elevation information calculated by the bilinear interpolation method, and the closer the i-th pixel is to the center area of ​​the vertex grid, the more likely the pixel is to be located at the bottom of the concave, and the smaller the elevation information of the pixel.

[0067] Based on the above analysis, the elevation information of the i-th pixel point in the vertex grid of the convex area is corrected, and the correction formula is: .in, represents the elevation information after the elevation information of the i-th pixel is corrected; is the original elevation information of the i-th pixel, that is, the elevation information obtained by bilinear interpolation; is a preset value greater than zero, and in this embodiment, the value is 2, which can be selected by the implementer according to the actual situation; p refers to the sequence composed of the Euclidean distances between the i-th pixel point and the four vertices in the vertex grid where it is located; Refers to the minimum value in the sequence p; Refers to the maximum value in the sequence p; Refers to the correction factor of the i-th pixel.

[0068] When the i-th pixel When the value is larger, the pixel is closer to the center of the vertex grid, and the pixel is more likely to be located at a higher bulge, so the elevation information of the i-th pixel is set to a larger value.

[0069] Similarly, the elevation information of the i-th pixel located in the vertex grid of the concave area is corrected, and the correction formula is: . When the i-th pixel The larger the value, the closer the pixel is to the center of the vertex grid, and the more likely the pixel is to be located in a lower depression, so the elevation information of the i-th pixel is set to a smaller value.

[0070] The elevation information of all remote sensing pixel points located in the convex vertex grid or the concave vertex grid is corrected, and the elevation information of the remote sensing pixel points not located in the convex vertex grid and the concave vertex grid is kept original, so as to obtain the elevation information of each remote sensing pixel point that is closer to the real height.

[0071] Based on the same inventive concept as the above method, an embodiment of the present application also provides a remote sensing image processing system that integrates DEM elevation information, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned remote sensing image processing methods that integrate DEM elevation information are implemented.

[0072] In summary, the embodiment of the present application obtains the DEM data and remote sensing image of the target area and matches them. Since each grid in the DEM data corresponds to multiple grids in the remote sensing image, in the process of fusing the DEM data and the remote sensing image, the grid center point in the DEM data is used as the vertex to obtain each vertex grid, which is helpful for the subsequent analysis of the concave and convex features of the terrain where the pixel point in the remote sensing image is located, and based on this, the elevation information corresponding to the pixel point is corrected; the internal remote sensing pixel point is obtained according to the positional relationship between the pixel center point and the vertex grid, which is helpful for distinguishing the vertex grid where the pixel point is located, making the subsequent analysis more accurate; the internal remote sensing pixel points in each vertex grid are classified based on the grayscale value, and the grayscale value is used to classify the internal remote sensing pixel points. According to the quantitative distribution characteristics of the classified internal remote sensing pixel points in the vertex grid, it is judged whether the vertex grid is the vertex grid to be distinguished; the vertex grids with concave-convex features are screened out to further narrow the range of pixel points that need to correct elevation information; according to the grayscale distribution of pixels in the neighborhood range of each internal remote sensing pixel point in the vertex grid to be distinguished, the concave-convex distinction value of each vertex grid to be distinguished is obtained, and the category to which each vertex grid to be distinguished belongs is obtained. The beneficial effect is that the vertex grids with concave-convex features are further divided, which is helpful for the subsequent correction of the elevation information of the pixel points according to different terrain features, so that the elevation information of the corrected remote sensing image can more accurately reflect the elevation, slope aspect and slope information of the open-pit mine.

[0073] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two continuous boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two continuous operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0074] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, no matter from which point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; the technical solutions recorded in the above embodiments are modified, or some of the technical features are replaced by equivalents, which does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A remote sensing image processing method integrating DEM elevation information, characterized in that: The method comprises the following steps: Obtain DEM data and remote sensing images of the target area; The DEM data and remote sensing images of the target area are matched, and each vertex grid is obtained by taking the center point of the grid in the DEM data as the vertex; the internal remote sensing pixel points are obtained according to the positional relationship between the pixel center point in the remote sensing image and the vertex grid; the internal remote sensing pixel points in each vertex grid are classified based on the grayscale value, and the vertex grid is judged whether it is a vertex grid to be distinguished according to the quantitative distribution characteristics of the classified internal remote sensing pixel points in the vertex grid; According to the grayscale distribution of the pixel points in the neighborhood of each internal remote sensing pixel point in the vertex grid to be distinguished, the concave-convex distinction value of each vertex grid to be distinguished is obtained, and the category to which each vertex grid to be distinguished belongs is obtained; The elevation information of each pixel is obtained; the elevation information of each pixel of the remote sensing image is corrected according to the category of each vertex grid to be distinguished and the position of the vertex in each vertex grid to be distinguished from each pixel.

2. A remote sensing image processing method integrating DEM elevation information as claimed in claim 1, characterized in that: The above method obtains each vertex grid as follows: The adjacent grid center points in the same row and the adjacent grid center points in the same column are connected by straight lines to obtain a vertex grid.

3. A remote sensing image processing method integrating DEM elevation information as claimed in claim 1, characterized in that: The process of obtaining the internal remote sensing pixel points is as follows: The remote sensing image pixel points whose pixel centers are within the vertex grid are regarded as the internal remote sensing pixel points of the vertex grid; If the center of a remote sensing image pixel belongs to an edge pixel of the vertex grid, the corresponding pixel is recorded as an internal remote sensing pixel.

4. A remote sensing image processing method integrating DEM elevation information as claimed in claim 1, characterized in that: The step of judging whether a vertex grid is a vertex grid to be distinguished is: Obtain the average gray value of the internal remote sensing pixel points of each vertex grid, which is recorded as the first segmentation threshold; The internal remote sensing pixel points in the vertex grid that are greater than the first segmentation threshold are regarded as the first type of pixel points; According to the quantity distribution characteristics of the first type of pixel points in each vertex grid, the concave-convex discrimination value of each vertex grid is obtained; The concave-convex discrimination values ​​of all vertex grids are subjected to a threshold segmentation algorithm to obtain a second segmentation threshold; The vertex grids whose concave-convex discrimination values ​​are greater than the second segmentation threshold are used as vertex grids to be distinguished.

5. A remote sensing image processing method integrating DEM elevation information as claimed in claim 4, characterized in that: The concave-convex discrimination value of each vertex grid is obtained as follows: The difference between the number of first-category pixels in the left half and the right half of the vertex grid is recorded as the first difference; the difference between the number of first-category pixels in the upper half and the lower half of the vertex grid is recorded as the second difference; The first difference and the second difference of each vertex grid are combined to obtain the concave-convex discrimination value of each vertex grid, wherein the concave-convex discrimination value is positively correlated with the first difference and the second difference.

6. A remote sensing image processing method integrating DEM elevation information as claimed in claim 1, characterized in that: The concave-convex distinction value of each vertex grid to be distinguished is obtained as follows: For each vertex grid to be distinguished, a neighborhood window is preset with each internal remote sensing pixel as the center; the information entropy of the grayscale values ​​of all pixels in the neighborhood window of each internal remote sensing pixel is obtained; A grayscale histogram of the vertex grid to be distinguished is obtained, the variance of the number of pixels corresponding to each grayscale level is calculated, and the ratio of the information entropy to the variance is used as the concave-convex distinction value of the vertex grid to be distinguished.

7. A remote sensing image processing method integrating DEM elevation information as claimed in claim 1, characterized in that: The method of obtaining the category of each vertex grid to be distinguished is as follows: Using a clustering algorithm to divide all vertex grids to be distinguished into two categories based on the concave-convex distinguishing value; calculating the average of the concave-convex distinguishing values ​​of all vertex grids to be distinguished in each category, and classifying the category with the largest average value as the convex area; The remaining category is classified as the concave area.

8. A remote sensing image processing method integrating DEM elevation information as claimed in claim 7, characterized in that: The process of correcting the elevation information of each pixel point of the remote sensing image is specifically as follows: According to the distance distribution of each pixel point from the four vertices in the vertex grid where it is located, combined with the elevation information of each pixel point, the correction factor of each pixel point is obtained; When the vertex grid where the pixel point is located is a convex area, the sum of the elevation information of the corresponding pixel point and the correction factor is used as the corrected elevation information of the pixel point; When the vertex grid where the pixel point is located is a concave area, the difference between the elevation information of the corresponding pixel point and the correction factor is used as the corrected elevation information of the pixel point; Otherwise, the elevation information of the corresponding pixel remains unchanged.

9. A remote sensing image processing method integrating DEM elevation information as claimed in claim 8, characterized in that: The correction factor for each pixel is obtained as follows: The ratio of the minimum distance to the maximum distance between each pixel point and the four vertices of the vertex grid where the pixel point is located is calculated; the product of the elevation information of each pixel point and the ratio and a preset value is calculated as a correction factor.

10. A remote sensing image processing system integrating DEM elevation information, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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