A hyperspectral remote sensing geological mapping control system and method

Abstract

The present application relates to the field of geological mapping technology, and specifically to a hyperspectral remote sensing geological mapping control system and method, the method comprising: continuously collecting the same geology to obtain each frame of remote sensing grayscale image; determining the local grayscale difference between any non-edge pixel and any edge pixel in each frame of remote sensing grayscale image; determining the first grayscale distribution coefficient and the second grayscale distribution coefficient of each frame of remote sensing grayscale image; determining the quality assessment coefficient of each frame of remote sensing grayscale image; determining the edge distribution coefficient of each frame of remote sensing grayscale image; determining the edge contribution weight of each frame of remote sensing grayscale image; determining the anti-noise ability coefficient of each frame of remote sensing grayscale image; determining the clarity assessment index of each frame of remote sensing grayscale image; obtaining the best remote sensing grayscale image, deblurring it, and obtaining a hyperspectral remote sensing geological mapping image. The present application can improve the image quality of hyperspectral remote sensing mapping images and ensure the effect of improving both spectral resolution and spatial resolution.

Description

Technical Field

[0001] The present application relates to the technical field of geological surveying and mapping, and in particular to a hyperspectral remote sensing geological surveying and mapping control system and method. Background Art

[0002] Hyperspectral remote sensing geological mapping uses remote sensing images to determine the geological structure, rock structure, hydrogeology and distribution of mineral resources in a certain area to be measured. Hyperspectral remote sensing technology is different from traditional spectral remote sensing technology that uses color differences to mark targets. Instead, it is a multi-dimensional information acquisition technology that combines imaging technology and spectral technology. It has more flexible selectivity and variability, which significantly improves the ability to distinguish and identify the geological structure of the area to be measured.

[0003] The application of remote sensing technology in geology enables geologists to have an overview of the earth, thus providing important data basis for studying and understanding the structural structure of the entire crust and its development and evolution trends. However, hyperspectral remote sensing geological mapping often requires a trade-off between spectral resolution and spatial resolution. It is impossible to ensure that the spatial resolution is high while the spectral resolution is high, resulting in low quality of the hyperspectral remote sensing geological mapping images obtained. Summary of the invention

[0004] In order to solve the above technical problems, a hyperspectral remote sensing geological mapping control system and method are provided to solve the existing problems.

[0005] The solution to the technical problem of this application is to provide a hyperspectral remote sensing geological mapping control system and method, including the following steps:

[0006] In a first aspect, an embodiment of the present application provides a hyperspectral remote sensing geological mapping control method, the method comprising the following steps:

[0007] Continuously collect the same geological data to obtain each frame of remote sensing grayscale image; extract each edge contour in each frame of remote sensing grayscale image to distinguish edge pixels from non-edge pixels;

[0008] According to the difference in grayscale values ​​between any non-edge pixel point, any edge pixel point and the pixel points in their local neighborhood in each frame of remote sensing grayscale image, respectively determine the local grayscale difference of any non-edge pixel point and any edge pixel point in each frame of remote sensing grayscale image; analyze the degree of deviation of the local grayscale difference of non-edge pixel points and edge pixel points at different grayscale levels, and respectively determine the first grayscale distribution coefficient and the second grayscale distribution coefficient of each frame of remote sensing grayscale image;

[0009] Determining a quality assessment coefficient of each frame of remote sensing grayscale image based on the first grayscale distribution coefficient and the second grayscale distribution coefficient;

[0010] According to the discrete distribution between different edge contours in each frame of remote sensing grayscale image, the edge distribution coefficient of each frame of remote sensing grayscale image is determined; the difference between the number of edge pixels and non-edge pixels at the same grayscale level is analyzed to determine the edge contribution weight of each frame of remote sensing grayscale image; according to the number of edge contours in each frame of remote sensing grayscale image, the anti-noise ability coefficient of each frame of remote sensing grayscale image is determined in combination with the edge distribution coefficient and the edge contribution weight;

[0011] Based on the quality assessment coefficient and the anti-noise capability coefficient, a clarity assessment index of each frame of remote sensing grayscale image is determined; based on the clarity assessment index, an optimal remote sensing grayscale image is obtained, and a deblurring process is performed on the image to obtain a hyperspectral remote sensing geological mapping image.

[0012] Preferably, the determining of the local grayscale difference between any non-edge pixel point and any edge pixel point in each frame of remote sensing grayscale image includes:

[0013] The difference between the grayscale value of any non-edge pixel point, any edge pixel point and the grayscale value of the remaining pixels in the neighborhood of each frame of remote sensing grayscale image is recorded as a first difference and a second difference, respectively;

[0014] Taking the mean of the first difference between any non-edge pixel and all other pixels in its neighborhood as the local grayscale difference of any non-edge pixel in each frame of remote sensing grayscale image;

[0015] The average of the second difference between any edge pixel point and all other pixel points in its neighborhood is used as the local grayscale difference of any edge pixel point in each frame of remote sensing grayscale image.

[0016] Preferably, the first grayscale distribution coefficient and the second grayscale distribution coefficient of each frame of remote sensing grayscale image include:

[0017] The mean of the local grayscale differences of all non-edge pixels with equal grayscale values ​​in each frame of remote sensing grayscale image is recorded as the first mean of each grayscale level corresponding to the non-edge pixels;

[0018] Taking the variance of the first mean values ​​of all gray levels corresponding to non-edge pixels in each frame of remote sensing gray image as the first gray distribution coefficient of each frame of remote sensing gray image;

[0019] The mean of the local grayscale differences of all edge pixels with equal grayscale values ​​in each frame of remote sensing grayscale image is recorded as the second mean of each grayscale level corresponding to the edge pixels;

[0020] The variance of the second mean of all gray levels corresponding to edge pixels in each frame of remote sensing gray image is used as the second gray distribution coefficient of each frame of remote sensing gray image.

[0021] Preferably, the quality assessment coefficient of each frame of remote sensing grayscale image is the ratio of the first grayscale distribution coefficient to the second grayscale distribution coefficient.

[0022] Preferably, the step of determining the edge distribution coefficient of each frame of remote sensing grayscale image comprises:

[0023] Perform curve fitting on the position coordinates of all edge pixel points on each edge contour in each frame of remote sensing grayscale image to obtain the centroid coordinates of the fitting curve corresponding to each edge contour;

[0024] The discrete degree of the distance between the centroid coordinates of all two arbitrary edge contours in each frame of remote sensing grayscale image is calculated as the edge distribution coefficient of each frame of remote sensing grayscale image.

[0025] Preferably, the step of determining the edge contribution weight of each frame of remote sensing grayscale image comprises:

[0026] Respectively counting the number of all edge pixels and the number of all non-edge pixels at each gray level corresponding to the edge pixels in each frame of the remote sensing grayscale image, and recording them as the first number and the second number respectively;

[0027] Calculate the ratio of the first quantity at each gray level corresponding to the edge pixel points in each frame of remote sensing grayscale image to the second quantity at the same gray level, and record it as the quantity ratio; normalize the sum of the quantity ratios at all gray levels corresponding to the edge pixel points in each frame of remote sensing grayscale image as the edge contribution weight of each frame of remote sensing grayscale image.

[0028] Preferably, the step of determining the anti-noise capability coefficient of each frame of remote sensing grayscale image comprises:

[0029] The number of all edge contours in each frame of remote sensing grayscale image is counted and recorded as the edge number; the product of the edge number, the edge distribution coefficient and the edge contribution weight is used as the anti-noise ability coefficient of each frame of remote sensing grayscale image.

[0030] Preferably, the determining of the clarity evaluation index of each frame of remote sensing grayscale image includes:

[0031] The calculation result of the exponential function with the natural constant as the base and the quality assessment coefficient as the exponent is calculated; and the normalized result of the product of the calculation result and the anti-noise capability coefficient is used as the clarity assessment index of each frame of the remote sensing grayscale image.

[0032] Preferably, the obtaining of the best remote sensing grayscale image includes: arranging all remote sensing grayscale images in order of acquisition time, and recording the first remote sensing grayscale image whose clarity evaluation index is greater than or equal to a preset threshold as the best remote sensing grayscale image.

[0033] In the second aspect, an embodiment of the present application also provides a hyperspectral remote sensing geological mapping control system, 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-mentioned hyperspectral remote sensing geological mapping control methods are implemented.

[0034] This application has at least the following beneficial effects:

[0035] The present application determines the local grayscale difference of any non-edge pixel point and any edge pixel point in each frame of remote sensing grayscale image according to the difference in grayscale values ​​between any non-edge pixel point and any edge pixel point in each frame of remote sensing grayscale image and the pixels in their local neighborhood. The beneficial effect lies in that the local grayscale difference between non-edge pixel points and edge pixel points is taken into account, reflecting the smoothness of the grayscale change of non-edge pixel points and the grayscale significant features of edge pixel points; the deviation degree of the local grayscale difference of non-edge pixel points and edge pixel points at different grayscale levels is analyzed to determine the first grayscale distribution coefficient and the second grayscale distribution coefficient of each frame of remote sensing grayscale image respectively; based on the first grayscale distribution coefficient and the second grayscale distribution coefficient, the local grayscale difference between non-edge pixel points and edge pixel points is determined. The quality evaluation coefficient of each frame of remote sensing grayscale image is determined by the distribution coefficient of non-edge pixels and edge pixels corresponding to different grayscale levels, so as to reflect the smoothness of grayscale connection between non-edge pixels, so that the non-edge pixels in the non-edge area have better spatial connectivity in visual perception, the grayscale color change of the image looks smoother, the clarity of the non-edge area of ​​the image, and the drastic change of grayscale color between edge pixels make the edge contour more prominent and the clarity of the edge area of ​​the image; the edge distribution coefficient of each frame of remote sensing grayscale image is determined according to the discrete distribution between different edge contours in each frame of remote sensing grayscale image, and its beneficial effect is The method takes into account the distribution of different contour edges to reflect the spatial information contained in the image, and then reflects the quality of the image; analyzes the difference in the number of edge pixels and non-edge pixels at the same grayscale level to determine the edge contribution weight of each frame of remote sensing grayscale image. The beneficial effect is that the difference in the number of edge pixels and non-edge pixels at the same grayscale level is taken into account to reflect the importance of the corresponding contour edge to the detail information analysis of the image; according to the number of edge contours in each frame of remote sensing grayscale image, combined with the edge distribution coefficient and the edge contribution weight, the anti-noise ability coefficient of each frame of remote sensing grayscale image is determined. The beneficial effect is that it takes into account the multiple feature information contained in the image to illustrate that the image contains detail information. The degree of section information reflects the quality of the image; based on the quality assessment coefficient and the anti-noise ability coefficient, the clarity assessment index of each frame of remote sensing grayscale image is determined; based on the clarity assessment index, the best remote sensing grayscale image is obtained, and deblurring is performed on it to obtain a hyperspectral remote sensing geological mapping image. The beneficial effect is that based on the clarity assessment index, the best remote sensing grayscale image is selected to ensure that the selected remote sensing grayscale image has a higher quality before deblurring, avoids the spatial resolution of the selected remote sensing grayscale image being too low and the image being too blurred, affecting the effect of deblurring, can improve the image quality of the hyperspectral remote sensing mapping image, and ensure the effect of improving both the spectral resolution and the spatial resolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] A hyperspectral remote sensing geological mapping control method of the present application is further described in detail below in conjunction with the accompanying drawings.

[0037] Figure 1 A flowchart of the steps of a hyperspectral remote sensing geological mapping control method provided in an embodiment of the present application;

[0038] Figure 2 A flowchart of the steps of a method for obtaining a quality assessment coefficient of each frame of remote sensing grayscale image provided in an embodiment of the present application;

[0039] Figure 3 A flowchart of the steps of a method for obtaining the anti-noise capability coefficient of each frame of remote sensing grayscale image provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of this application more clear, the following is a further detailed description of a hyperspectral remote sensing geological mapping control system and method proposed in this application in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.

[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] See also Figure 1 , which shows a step flow chart of a hyperspectral remote sensing geological mapping control method provided by an embodiment of the present application, the method comprising the following steps:

[0043] Step 1: Continuously collect the same geology to obtain each frame of remote sensing grayscale image.

[0044] In the process of remote sensing geological mapping, it is necessary to analyze the overall geological situation, so hyperspectral images are usually used to analyze the geological conditions. The spectral range of each band of the hyperspectral image is relatively narrow. In order to reduce the interference caused by the shooting process, the photosensitive unit size of the satellite sensor is often larger, which means that the instantaneous field of view of the satellite sensor is larger when shooting, resulting in lower spatial resolution of the image. In this process, although it has a higher spectral resolution, the low spatial resolution makes the image blurred, which in turn leads to poor quality of the surveying and mapping image.

[0045] Due to the mutual checks and balances between the spectral resolution and spatial resolution of mapping images, the spectral resolution of mapping images is often high while the spatial resolution is low, making it difficult for the final mapping image quality to reach the ideal state. In order to improve the quality of the captured remote sensing grayscale images, the maximum fuzzy state that the spatial resolution can bear is analyzed according to the changes in the spectral resolution of the mapping images at different times, and then the mapping images with the maximum fuzzy state of the spatial resolution are clarified to improve the quality of the captured remote sensing grayscale images.

[0046] Based on the above analysis, the geology at the same location is continuously photographed by satellite remote sensing sensors, and remote sensing mapping images with different spectral resolutions are collected according to the spectral resolution from high to low, and multiple frames of remote sensing mapping images are obtained. The collected remote sensing mapping images are denoised and grayscaled to obtain continuous remote sensing grayscale images of each frame.

[0047] Preferably, in this embodiment, a mean filtering method is used for noise reduction processing, wherein the mean filtering method is a well-known technology and will not be described in detail here. As other implementation methods, the implementer may adopt other methods of the prior art, such as the median filtering algorithm, etc. This embodiment does not impose any special restrictions on this.

[0048] It should be noted that in order to compare the remote sensing grayscale images later and reduce analysis errors, when photographing the geology at the same location, the position of the satellite remote sensing sensor should be kept unchanged as much as possible.

[0049] At this point, each frame of continuous remote sensing grayscale images of the same geology is obtained.

[0050] Step 2: According to the difference in grayscale values ​​between any non-edge pixel point, any edge pixel point and the pixels in its local neighborhood in each frame of remote sensing grayscale image, respectively determine the local grayscale difference of any non-edge pixel point and any edge pixel point in each frame of remote sensing grayscale image; analyze the degree of deviation of the local grayscale difference of non-edge pixels and edge pixels at different grayscale levels, and respectively determine the first grayscale distribution coefficient and the second grayscale distribution coefficient of each frame of remote sensing grayscale image; based on the first grayscale distribution coefficient and the second grayscale distribution coefficient, determine the quality assessment coefficient of each frame of remote sensing grayscale image.

[0051] In the prior art, in the process of hyperspectral remote sensing geological mapping, spectral resolution and spatial resolution are generally balanced by selecting a suitable setting value. Such setting value is often relatively conservative. If the spectral resolution is low, it will take a long time to obtain data, but if the spatial resolution is low, the image will not be clear enough.

[0052] Based on the above analysis, by making the spectral resolution as large as possible, the spatial resolution will definitely become smaller, and the image will definitely become blurred. Then, by clarifying the blurred image, both the spectral resolution and the spatial resolution can be improved. However, not all blurred images can be effectively clarified. For example, when the spectral resolution is large enough, the spatial resolution is too small, and the image is blurred to the point of losing its original features, and it is difficult to clarify it. Therefore, it is particularly important to find the relative balance between spectral resolution and spatial resolution more accurately.

[0053] Based on the above analysis, in order to obtain hyperspectral images, it is necessary to increase the number of bands to improve the spectral resolution. Due to the optical design of the remote sensing sensor and the limitations of the sensor array, the spectral information collected at each spatial position is constrained, resulting in a reduction in the light energy received by the remote sensing sensor in each band, thereby reducing the spatial resolution, and the resulting image is relatively blurred, and the image quality and noise resistance are significantly weakened; similarly, the quality and noise resistance of continuous adjacent frames of remote sensing grayscale images are analyzed to determine the changing trend of image quality. When the quality of remote sensing grayscale images is high, it is necessary to effectively and clearly display the different geology in the image, and different areas in the image need to have obvious contrast, that is, the color distribution between non-edge pixels has obvious spatial continuity, and the color of edge pixels and non-edge pixels needs to be significantly different. Therefore, the spatial continuity of non-edge pixels in remote sensing grayscale images and the distribution differences between edge pixels and non-edge pixels are analyzed to reflect the image quality of remote sensing grayscale images.

[0054] Furthermore, the step flow chart of the method for obtaining the quality assessment coefficient of each frame of remote sensing grayscale image provided in the embodiment of the present application is as follows: Figure 2 shown.

[0055] First, distinguish the edge pixels and non-edge pixels in the remote sensing grayscale image, specifically:

[0056] Perform edge detection on each frame of remote sensing grayscale image to obtain edge pixel points of each edge contour;

[0057] Preferably, in this embodiment, the Canny edge detection algorithm is used for edge detection, wherein the Canny edge detection algorithm is a well-known technology and will not be described in detail here. As other implementation methods, the implementer may adopt other methods of the prior art, such as the Sobel operator, etc. This embodiment does not impose any special restrictions on this.

[0058] The remaining pixels except edge pixels in each frame of remote sensing grayscale image are recorded as non-edge pixels;

[0059] Furthermore, the grayscale distribution between edge pixels and non-edge pixels in each frame of remote sensing grayscale image is analyzed, specifically:

[0060] The difference between the grayscale value of any non-edge pixel point in each frame of remote sensing grayscale image and the grayscale values ​​of all other pixels in its neighborhood is recorded as the first difference; the average of the first differences between any non-edge pixel point and all other pixels in its neighborhood is taken as the local grayscale difference of any non-edge pixel point;

[0061] Preferably, in this embodiment, the absolute value of the difference between the grayscale value of any non-edge pixel point in each frame of remote sensing grayscale image and the grayscale values ​​of all other pixels in its 8-neighborhood is recorded as the first difference; the average of the first difference between any non-edge pixel point and all other pixels in its 8-neighborhood is taken as the local grayscale difference of any non-edge pixel point.

[0062] Preferably, in this embodiment, the method for calculating the local grayscale difference of any non-edge pixel point in each frame of remote sensing grayscale image is: ,in, For the Frame remote sensing grayscale image The local grayscale difference of non-edge pixels, For the Frame remote sensing grayscale image The gray value of non-edge pixels, For the Frame remote sensing grayscale image The neighborhood of non-edge pixels The gray value of a pixel.

[0063] The difference between the grayscale value of any edge pixel point in each frame of remote sensing grayscale image and the grayscale values ​​of all other pixels in its neighborhood is recorded as the second difference; the average of the second differences between any edge pixel point and all other pixels in its neighborhood is taken as the local grayscale difference of any edge pixel point;

[0064] Preferably, in this embodiment, the absolute value of the difference between the grayscale value of any edge pixel point in each frame of remote sensing grayscale image and the grayscale values ​​of all other pixels in its 8-neighborhood is recorded as the second difference; the average of the second difference between any edge pixel point and all other pixels in its 8-neighborhood is taken as the local grayscale difference of any edge pixel point.

[0065] The mean of the local grayscale differences of all non-edge pixels with equal grayscale values ​​in each frame of remote sensing grayscale image is recorded as the first mean of each grayscale level corresponding to the non-edge pixels;

[0066] Taking the variance of the first mean values ​​of all gray levels corresponding to non-edge pixels in each frame of remote sensing gray image as the first gray distribution coefficient of each frame of remote sensing gray image;

[0067] The mean of the local grayscale differences of all edge pixels with equal grayscale values ​​in each frame of remote sensing grayscale image is recorded as the second mean of each grayscale level corresponding to the edge pixels;

[0068] The variance of the second mean of all gray levels corresponding to edge pixels in each frame of remote sensing gray image is used as the second gray distribution coefficient of each frame of remote sensing gray image;

[0069] It should be noted that in the non-edge area of ​​the remote sensing grayscale image, each grayscale level provides a different color reference. When the grayscale values ​​of the non-edge pixels corresponding to each grayscale level are relatively close to the grayscale values ​​of the other pixels in the surrounding 8 neighborhoods, that is, the smaller the local grayscale difference of any edge pixel, the smaller the first mean value, indicating that the local difference of the non-edge pixels corresponding to the grayscale level is small, and the smaller the first grayscale distribution coefficient, the smoother the connection between the non-edge pixels corresponding to different grayscale levels, and the image has better spatial connectivity in the visual perception of the non-edge area, and has better clarity. The image color changes look smoother, and the image is clearer in the non-edge area; secondly, for the edge area of ​​the remote sensing grayscale image, since the distribution between the edge pixels corresponding to different grayscale levels in the edge contour area is more extreme, the color usually changes dramatically, which is significantly different from the non-edge area, so it has a strong sense of fragmentation in visual perception. This high contrast makes the edge contour appear clear. Therefore, the larger the second grayscale distribution coefficient, the more prominent the edge contour and the clearer the edge contour.

[0070] Further, based on the first grayscale distribution coefficient and the second grayscale distribution coefficient, a quality assessment coefficient is determined, specifically:

[0071] Using the ratio of the first grayscale distribution coefficient to the second grayscale distribution coefficient as a quality assessment coefficient for each frame of remote sensing grayscale image;

[0072] It should be noted that the smaller the first grayscale distribution coefficient is, the clearer the non-edge area is, and the larger the second grayscale distribution coefficient is, the clearer the edge contour area is. Therefore, the smaller the obtained quality assessment coefficient is, the higher the quality of the corresponding remote sensing grayscale image is.

[0073] At this point, the quality assessment coefficient of each frame of remote sensing grayscale image is obtained.

[0074] Step 3: determine the edge distribution coefficient of each frame of remote sensing grayscale image according to the discrete distribution between different edge contours in each frame of remote sensing grayscale image; analyze the difference in the number of edge pixels and non-edge pixels at the same grayscale level to determine the edge contribution weight of each frame of remote sensing grayscale image; determine the anti-noise ability coefficient of each frame of remote sensing grayscale image according to the number of edge contours in each frame of remote sensing grayscale image, combined with the edge distribution coefficient and the edge contribution weight.

[0075] Furthermore, remote sensing grayscale images generally have more detailed descriptions. Due to the high shooting altitude, the information obtained is relatively small in the remote sensing grayscale images, and slight noise will cause greater loss to these small information; therefore, the stronger the anti-noise ability of the remote sensing grayscale image, the smaller the impact of slight noise. The anti-noise ability of the remote sensing grayscale image generally depends on whether it has more features. The anti-noise ability of the image is analyzed by analyzing the number and distribution of edge contours in the remote sensing grayscale image and the contribution of each edge contour to the remote sensing grayscale image, so as to further reflect the image quality of the remote sensing grayscale image.

[0076] Further, the step flow chart of the method for obtaining the anti-noise capability coefficient of each frame of remote sensing grayscale image provided by the embodiment of the present application is as follows: Figure 3 shown.

[0077] First, the distribution of different edge contours in each frame of remote sensing grayscale image is analyzed to determine the edge distribution coefficient, which is:

[0078] Perform curve fitting on the position coordinates of all edge pixel points on each edge contour in each frame of remote sensing grayscale image to obtain the centroid coordinates of the fitting curve corresponding to each edge contour;

[0079] Preferably, in this embodiment, the least square method is used for curve fitting, wherein the least square method is a well-known technique and will not be described in detail herein.

[0080] It should be noted that the calculation of centroid coordinates is a well-known technique and will not be described in detail here.

[0081] Calculate the discrete degree of the distance between the centroid coordinates of all two arbitrary edge contours in each frame of remote sensing grayscale image as the edge distribution coefficient of each frame of remote sensing grayscale image;

[0082] Preferably, in this embodiment, the information entropy of the L2 norm between the centroid coordinates of all two arbitrary edge contours in each frame of remote sensing grayscale image is calculated as the edge distribution coefficient of each frame of remote sensing grayscale image, wherein the calculation method of the L2 norm, i.e., the Euclidean distance and the calculation method of the information entropy are well-known technologies and will not be repeated here. As other implementation methods, the implementer may adopt other methods of the prior art, for example, Manhattan distance to measure distance and coefficient of variation to measure the degree of dispersion, and this embodiment does not impose any special restrictions on this.

[0083] It should be noted that, in the remote sensing grayscale image, the distribution of the edge contour is reflected by analyzing the distance between the centroid coordinates of different edge contours. If the edge contour distribution is more concentrated, the smaller the edge distribution coefficient is, the less detailed information is contained in the area of ​​the edge contour of the remote sensing grayscale image. In this case, the image quality is poor. If the edge contour distribution is more discrete, that is, the larger the edge distribution coefficient is, the more uniform the edge contour distribution of the remote sensing grayscale image is, which can provide more spatial information. In this case, the image quality of the remote sensing grayscale image is relatively good.

[0084] Furthermore, the grayscale value of the edge pixel on the edge contour changes more significantly than the grayscale value of the non-edge pixel. The difference in the number of edge pixels and non-edge pixels at the same grayscale level is analyzed to reflect the contribution of the edge pixels on the edge contour in the remote sensing grayscale image to the image analysis and determine the edge contribution weight, which is specifically:

[0085] Count the number of all edge pixels at each gray level corresponding to the edge pixels in each frame of remote sensing gray image, recorded as the first number, and count the number of all non-edge pixels at each gray level corresponding to the edge pixels in each frame of remote sensing gray image, recorded as the second number;

[0086] Calculate the ratio of the first quantity at each gray level corresponding to the edge pixel point in each frame of remote sensing gray image to the second quantity at the same gray level, record it as the quantity ratio, and normalize the sum of the quantity ratios at all gray levels corresponding to the edge pixel point in each frame of remote sensing gray image as the edge contribution weight of each frame of remote sensing gray image;

[0087] Preferably, in this embodiment, a sigmoid function is used for normalization processing. As other implementation methods, implementers may use other methods in the prior art, such as a tanh function, for normalization processing, and this embodiment does not impose any special restrictions on this.

[0088] It should be noted that, in general, at the grayscale corresponding to the edge pixel, the pixel is almost on the edge, and only a very small number of non-edge pixels with the same grayscale will appear in the non-edge area. However, when the grayscale values ​​of the pixels in a certain area are relatively small, the grayscale values ​​of the edge pixels in the area are smaller than those of the edge pixels of other edge contours, but larger than the grayscale values ​​of the non-edge pixels in the area. If the number of edge pixels and non-edge pixels is close at the same grayscale, then at the corresponding grayscale, analyzing the features of the edge contour cannot better reflect the detailed information of the image. The larger the edge contribution weight, the greater the difference in the number of edge pixels and non-edge pixels at the same grayscale, the more edge pixels there are, and the corresponding edge contour features can better reflect the information of the image, and the greater the contribution of analyzing the edge contour to reflecting the detailed information of the image.

[0089] Further, based on the edge distribution coefficient and the edge contribution weight, the noise immunity coefficient is determined, specifically:

[0090] Counting the number of all edge contours in each frame of remote sensing grayscale image, recording it as the number of edges; taking the product of the number of edges, the edge distribution coefficient and the edge contribution weight as the anti-noise ability coefficient of each frame of remote sensing grayscale image;

[0091] It should be noted that the greater the number of edges, the greater the number of edge contours in the remote sensing grayscale image, the more detailed information the remote sensing grayscale image has, the richer the structural changes and texture features in the image, and the greater the edge distribution coefficient, the more uniform the edge contour distribution of the remote sensing grayscale image can provide more spatial information. The greater the edge contribution weight, the more important the edge contour is in reflecting the detailed information of the image. The greater the obtained anti-noise coefficient, the more feature information is contained in the remote sensing grayscale image, reflecting that the remote sensing grayscale image has more detailed information, the stronger its anti-noise ability is, and the better the image quality.

[0092] At this point, the anti-noise ability coefficient of each frame of remote sensing grayscale image is obtained.

[0093] Step 4: Determine the clarity evaluation index of each frame of remote sensing grayscale image based on the quality evaluation coefficient and the anti-noise capability coefficient; obtain the best remote sensing grayscale image based on the clarity evaluation index, perform deblurring processing on it, and obtain a hyperspectral remote sensing geological mapping image.

[0094] Further, based on the quality assessment coefficient and the anti-noise capability coefficient, a clarity assessment index is determined, and the best remote sensing grayscale image is selected and deblurred, specifically:

[0095] Calculate the calculation result of an exponential function with a natural constant as the base and the quality assessment coefficient as the exponent, and use the normalized result of the product of the calculation result and the anti-noise capability coefficient as the clarity assessment index of each frame of remote sensing grayscale image;

[0096] Preferably, in this example, a sigmoid function is used for normalization processing. As other implementations, implementers may use other methods in the prior art, such as a tanh function, for normalization processing, and this embodiment does not impose any special restrictions on this.

[0097] It should be noted that the larger the clarity assessment index is, the better the quality of the corresponding remote sensing grayscale image is, and the image contains more detail information.

[0098] All remote sensing grayscale images are arranged in order of acquisition time, and the first remote sensing grayscale image whose clarity evaluation index is greater than or equal to a preset threshold is recorded as the best remote sensing grayscale image;

[0099] Preferably, in this embodiment, the preset threshold value is 0.7. As other implementation modes, the implementer can set it according to the actual situation.

[0100] It should be noted that when the clarity assessment index is greater than or equal to the preset threshold, the spectral resolution of the remote sensing grayscale image is relatively high, and the low spatial resolution causes the blur degree of the corresponding remote sensing grayscale image to be able to withstand the maximum blur degree. Selecting the remote sensing grayscale image at this time can better perform clarity processing, avoiding the situation where the spatial resolution of the selected remote sensing grayscale image is too low and the image is too blurred, which affects the deblurring effect.

[0101] The optimal remote sensing grayscale image is deblurred to make it clear, thereby obtaining a hyperspectral remote sensing geological mapping image, thereby improving the spectral resolution and spatial resolution of the remote sensing geological mapping image.

[0102] Preferably, in this embodiment, the DeblurGANv2 algorithm is used for deblurring, wherein the DeblurGANv2 algorithm is a well-known technology and will not be described in detail here. As other implementation methods, the implementer may adopt other methods of the prior art, such as blind deconvolution, etc. This embodiment does not impose any special restrictions on this.

[0103] Based on the same inventive concept as the above method, an embodiment of the present application also provides a hyperspectral remote sensing geological mapping control system, 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-mentioned hyperspectral remote sensing geological mapping control methods are implemented.

[0104] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A hyperspectral remote sensing geological mapping control method, characterized in that: The method comprises the following steps: Continuously collect the same geological data to obtain each frame of remote sensing grayscale image; extract each edge contour in each frame of remote sensing grayscale image to distinguish edge pixels from non-edge pixels; According to the difference in grayscale values ​​between any non-edge pixel point, any edge pixel point and the pixel points in their local neighborhood in each frame of remote sensing grayscale image, respectively determine the local grayscale difference of any non-edge pixel point and any edge pixel point in each frame of remote sensing grayscale image; analyze the degree of deviation of the local grayscale difference of non-edge pixel points and edge pixel points at different grayscale levels, and respectively determine the first grayscale distribution coefficient and the second grayscale distribution coefficient of each frame of remote sensing grayscale image; Determining a quality assessment coefficient of each frame of remote sensing grayscale image based on the first grayscale distribution coefficient and the second grayscale distribution coefficient; According to the discrete distribution between different edge contours in each frame of remote sensing grayscale image, the edge distribution coefficient of each frame of remote sensing grayscale image is determined; the difference between the number of edge pixels and non-edge pixels at the same grayscale level is analyzed to determine the edge contribution weight of each frame of remote sensing grayscale image; according to the number of edge contours in each frame of remote sensing grayscale image, the anti-noise ability coefficient of each frame of remote sensing grayscale image is determined in combination with the edge distribution coefficient and the edge contribution weight; Based on the quality assessment coefficient and the anti-noise capability coefficient, a clarity assessment index of each frame of remote sensing grayscale image is determined; based on the clarity assessment index, an optimal remote sensing grayscale image is obtained, and a deblurring process is performed on the image to obtain a hyperspectral remote sensing geological mapping image.

2. A hyperspectral remote sensing geological mapping control method as claimed in claim 1, characterized in that: The determining of the local grayscale difference between any non-edge pixel point and any edge pixel point in each frame of remote sensing grayscale image includes: The difference between the grayscale value of any non-edge pixel point, any edge pixel point and the grayscale value of the remaining pixels in the neighborhood of each frame of remote sensing grayscale image is recorded as a first difference and a second difference, respectively; Taking the mean of the first difference between any non-edge pixel and all other pixels in its neighborhood as the local grayscale difference of any non-edge pixel in each frame of remote sensing grayscale image; The average of the second difference between any edge pixel point and all other pixel points in its neighborhood is used as the local grayscale difference of any edge pixel point in each frame of remote sensing grayscale image.

3. A hyperspectral remote sensing geological mapping control method as claimed in claim 1, characterized in that: The first grayscale distribution coefficient and the second grayscale distribution coefficient of each frame of remote sensing grayscale image include: The mean of the local grayscale differences of all non-edge pixels with equal grayscale values ​​in each frame of remote sensing grayscale image is recorded as the first mean of each grayscale level corresponding to the non-edge pixels; Taking the variance of the first mean values ​​of all gray levels corresponding to non-edge pixels in each frame of remote sensing gray image as the first gray distribution coefficient of each frame of remote sensing gray image; The mean of the local grayscale differences of all edge pixels with equal grayscale values ​​in each frame of remote sensing grayscale image is recorded as the second mean of each grayscale level corresponding to the edge pixels; The variance of the second mean of all gray levels corresponding to edge pixels in each frame of remote sensing gray image is used as the second gray distribution coefficient of each frame of remote sensing gray image.

4. A hyperspectral remote sensing geological mapping control method as claimed in claim 1, characterized in that: The quality assessment coefficient of each frame of remote sensing grayscale image is the ratio of the first grayscale distribution coefficient to the second grayscale distribution coefficient.

5. A hyperspectral remote sensing geological mapping control method as claimed in claim 1, characterized in that: Determining the edge distribution coefficient of each frame of remote sensing grayscale image includes: Perform curve fitting on the position coordinates of all edge pixel points on each edge contour in each frame of remote sensing grayscale image to obtain the centroid coordinates of the fitting curve corresponding to each edge contour; The discrete degree of the distance between the centroid coordinates of all two arbitrary edge contours in each frame of remote sensing grayscale image is calculated as the edge distribution coefficient of each frame of remote sensing grayscale image.

6. A hyperspectral remote sensing geological mapping control method as claimed in claim 3, characterized in that: Determining the edge contribution weight of each frame of remote sensing grayscale image includes: Respectively counting the number of all edge pixels and the number of all non-edge pixels at each gray level corresponding to the edge pixels in each frame of the remote sensing grayscale image, and recording them as the first number and the second number respectively; Calculate the ratio of the first quantity at each gray level corresponding to the edge pixel points in each frame of remote sensing grayscale image to the second quantity at the same gray level, and record it as the quantity ratio; normalize the sum of the quantity ratios at all gray levels corresponding to the edge pixel points in each frame of remote sensing grayscale image as the edge contribution weight of each frame of remote sensing grayscale image.

7. A hyperspectral remote sensing geological mapping control method as claimed in claim 1, characterized in that: The step of determining the anti-noise capability coefficient of each frame of remote sensing grayscale image comprises: The number of all edge contours in each frame of remote sensing grayscale image is counted and recorded as the edge number; the product of the edge number, the edge distribution coefficient and the edge contribution weight is used as the anti-noise ability coefficient of each frame of remote sensing grayscale image.

8. A hyperspectral remote sensing geological mapping control method as claimed in claim 1, characterized in that: Determining the clarity evaluation index of each frame of remote sensing grayscale image includes: The calculation result of the exponential function with the natural constant as the base and the quality assessment coefficient as the exponent is calculated; and the normalized result of the product of the calculation result and the anti-noise capability coefficient is used as the clarity assessment index of each frame of the remote sensing grayscale image.

9. A hyperspectral remote sensing geological mapping control method as claimed in claim 1, characterized in that: The method of obtaining the best remote sensing grayscale image includes: arranging all remote sensing grayscale images in the order of acquisition time, and recording the first remote sensing grayscale image whose clarity evaluation index is greater than or equal to a preset threshold as the best remote sensing grayscale image.

10. A hyperspectral remote sensing geological mapping control system, 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 a hyperspectral remote sensing geological mapping control method as described in any one of claims 1 to 9 are implemented.

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