High-resolution remote sensing image transmission method and system applied to mineral resource exploration

Abstract

The application discloses a kind of high-resolution remote sensing image transmission methods and systems applied to mineral resources exploration, it is related to image processing technical field.The method comprises: obtaining the high-resolution remote sensing image to be transmitted;By discrete wavelet transform, high-resolution remote sensing image is converted from spatial domain to frequency domain, and the remote sensing frequency image corresponding to high-resolution remote sensing image is obtained;According to the pixel density of each image region in the high-frequency image of remote sensing frequency image, the fuzzy weight of each image region is set;According to the fuzzy weight of each image region, high-resolution remote sensing image is compressed, and compressed remote sensing image is obtained, to make transmission to compressed remote sensing image.The high-resolution remote sensing image transmission method applied to mineral resources exploration provided in the embodiment of the application can improve the compression effect of remote sensing image.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and in particular to a high-resolution remote sensing image transmission method and system applied to mineral resource exploration. BACKGROUND

[0002] Mineral resource exploration is an important foundation work for ensuring national resource security and economic development. With the rapid development of remote sensing technology, high-resolution remote sensing images are increasingly widely used in mineral resource exploration. However, the data volume of high-resolution remote sensing images is huge, and the transmission demand is high, and traditional remote sensing image transmission methods have many limitations.

[0003] In the prior art, a JPEG2000 image compression algorithm is used to compress high-resolution remote sensing images, and then the compressed remote sensing images are transmitted.

[0004] However, remote sensing images are usually multispectral images containing multiple bands. The boundary between the region of interest and the background region is relatively blurred, so that the region of interest and the background region cannot be accurately identified, resulting in poor compression effect of the remote sensing images. SUMMARY

[0005] The present application provides a high-resolution remote sensing image transmission method and system applied to mineral resource exploration, which can improve the compression effect of remote sensing images.

[0006] In a first aspect of the present application, a high-resolution remote sensing image transmission method applied to mineral resource exploration is provided, comprising:

[0007] obtaining a high-resolution remote sensing image to be transmitted;

[0008] converting the high-resolution remote sensing image from a spatial domain to a frequency domain by discrete wavelet transform to obtain a remote sensing frequency image corresponding to the high-resolution remote sensing image;

[0009] setting a blur weight of each image region according to a pixel point density of each image region in a high-frequency image of the remote sensing frequency image;

[0010] using a compression algorithm based on the blur weight to compress the high-resolution remote sensing image according to the blur weight of each image region to obtain a compressed remote sensing image, so as to transmit the compressed remote sensing image.

[0011] Further, the setting of the blur weight of each image region according to the pixel point density of each image region in the high-frequency image of the remote sensing frequency image comprises:

[0012] clustering each pixel point according to a neighborhood density of each pixel point in the high-frequency image of the remote sensing frequency image to obtain a plurality of image regions.

[0013] determine a region performance value of each of the image regions according to the neighborhood density of each of the pixel points in the image region, the region performance value being used to represent how much high-frequency information is contained in the image region;

[0014] determine a region attention degree of each of the image regions according to a difference between the region performance value of each of the image regions and the region performance value of the corresponding neighborhood region;

[0015] determine a blur degree between each of the image regions according to a difference between the region attention degrees and a difference between the gray scale values;

[0016] set a blur weight of each of the image regions according to the blur degree between each of the image regions.

[0017] Further, the neighborhood density of each of the pixel points in the high-frequency image of the remote sensing frequency image is used to cluster each of the pixel points to obtain a plurality of image regions, including:

[0018] filter out the high-frequency image from each of the remote sensing frequency images;

[0019] calculate the neighborhood density of each of the pixel points in the high-frequency image with each of the pixel points as the center;

[0020] cluster each of the pixel points according to the neighborhood density of each of the pixel points to obtain a plurality of the image regions.

[0021] Further, the region performance value of each of the image regions is determined according to the neighborhood density of each of the pixel points in the image region, including:

[0022] the following steps are performed for each of the image regions respectively:

[0023] obtain a target pixel point quantity of a target image region and a maximum pixel point quantity in each of the image regions, the target image region being any one of the image regions;

[0024] perform mean value calculation on the neighborhood density of each of the pixel points in the target image region to obtain a pixel point density of the target image region;

[0025] determine the region performance value of the target image region by using the pixel point density of the target image region, the target pixel point quantity and the maximum pixel point quantity.

[0026] Further, the region attention degree of each of the image regions is determined according to a difference between the region performance value of each of the image regions and the region performance value of the corresponding neighborhood region, including:

[0027] For each of the image regions, the following steps are performed respectively:

[0028] Obtaining the Euclidean distance between the target image region and each of the adjacent regions, the target image region being any one of the image regions;

[0029] Taking the absolute value of the difference between the region performance value of the target image region and the region performance value of each of the adjacent regions, to obtain a plurality of region performance differences;

[0030] Using the Euclidean distance and the region performance difference, the region attention degree of the target image region is determined.

[0031] Further, the blur degree between each of the image regions is determined according to the difference between the region attention degrees and the difference between the gray values, comprising:

[0032] Obtaining the gray mean value of the first image region and the gray mean value of the second image region, the first image region and the second image region being any two different image regions in the image regions;

[0033] Taking the absolute value of the difference between the gray mean value of the first image region and the gray mean value of the second image region, to obtain a gray performance difference;

[0034] Taking the absolute value of the difference between the region attention degree of the first image region and the region attention degree of the second image region, to obtain an attention degree performance difference;

[0035] Using the gray performance difference and the attention degree performance difference, the blur degree between the first image region and the second image region is determined.

[0036] Further, the blur weight of each of the image regions is set according to the blur degree between each of the image regions, comprising:

[0037] For each of the image regions, the following steps are performed respectively:

[0038] Obtaining the plane distance between the target image region and each of the adjacent regions in the high-frequency image, the target image region being any one of the image regions;

[0039] Using the blur degree between the target image region and each of the adjacent regions and each of the plane distances, the blur weight of the target image region is determined.

[0040] The second aspect of the embodiment of the application provides a high-resolution remote sensing image transmission system applied to mineral resource exploration, comprising:

[0041] An image acquisition module is configured to acquire high-resolution remote sensing images to be transmitted.

[0042] An image conversion module is configured to convert the high-resolution remote sensing images from a spatial domain to a frequency domain by discrete wavelet transform to obtain remote sensing frequency images corresponding to the high-resolution remote sensing images.

[0043] A weight setting module is configured to set blur weights of image regions according to pixel point densities of the image regions in high-frequency images of the remote sensing frequency images.

[0044] An image compression module is configured to compress the high-resolution remote sensing images according to the blur weights of the image regions by using a compression algorithm based on the blur weights to obtain compressed remote sensing images, so that the compressed remote sensing images are transmitted.

[0045] In the high-resolution remote sensing image transmission method for mineral resource exploration provided by the embodiment of the present application, the high-resolution remote sensing images are converted from the spatial domain to the frequency domain by discrete wavelet transform to obtain remote sensing frequency images corresponding to the high-resolution remote sensing images. Then, the weights of the image regions are set according to the pixel point densities of the image regions in high-frequency images of the remote sensing frequency images. In this way, the regions of interest in the high-resolution remote sensing images can be identified by discrete wavelet transform. The weights of the image regions are set according to the differences in pixel point densities between the image regions of the regions of interest, which can ensure that the information of the regions of interest is retained as completely as possible. Finally, the high-resolution remote sensing images are compressed according to the blur weights of the image regions. In this way, the regions of interest are accurately identified and the information of the regions of interest is retained as completely as possible, so that the compression effect of the remote sensing images can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0047] Figure 1 A flowchart of a first high-resolution remote sensing image transmission method for mineral resource exploration provided by an embodiment of the present application;

[0048] Figure 2 A flowchart of a second high-resolution remote sensing image transmission method for mineral resource exploration provided by an embodiment of the present application;

[0049] Figure 3A structural schematic diagram of a high-resolution remote sensing image transmission system applied to mineral resource exploration is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object of the application, the following describes in detail the specific embodiments, structure, features and effects of a high-resolution remote sensing image transmission method and system applied to mineral resource exploration according to the present application, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0052] It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of the present application comply with the relevant provisions of laws and regulations.

[0053] It should be noted that in the embodiments of the present application, some existing industry solutions such as software, components, models, etc. may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0054] Mineral resource exploration is an important foundation work to ensure national resource security and economic development. With the rapid development of remote sensing technology, high-resolution remote sensing images are increasingly widely used in mineral resource exploration. However, high-resolution remote sensing images have a huge amount of data and high transmission requirements, and traditional remote sensing image transmission methods have many limitations.

[0055] In the existing method, the high-resolution remote sensing image is compressed using the JPEG2000 image compression algorithm, and then the compressed remote sensing image is transmitted. However, the remote sensing image is usually a multi-spectral image containing multiple bands. The boundary between the region of interest and the background region is relatively blurred, so that the region of interest and the background region cannot be accurately identified, resulting in poor compression effect of the remote sensing image.

[0056] The application aims to provide a high-resolution remote sensing image transmission method and system applied to mineral resource exploration. In the high-resolution remote sensing image transmission method applied to mineral resource exploration provided by the application, the high-resolution remote sensing image is first converted from a spatial domain to a frequency domain through discrete wavelet transform to obtain a remote sensing frequency image corresponding to the high-resolution remote sensing image. Then, the weight of each image region is set according to the pixel point density of each image region in the high-frequency image of the remote sensing frequency image. In this way, the region of interest in the high-resolution remote sensing image can be identified through the discrete wavelet transform. And the weight of each image region is set according to the difference in pixel point density between each image region in the region of interest, which can ensure that the information of the region of interest is retained as completely as possible. Finally, the high-resolution remote sensing image is compressed according to the fuzzy weight of the image region. In this way, the compression effect of the remote sensing image can be improved by accurately identifying the region of interest and retaining the information of the region of interest as completely as possible.

[0057] The application aims to provide a high-resolution remote sensing image transmission method and system applied to mineral resource exploration.

[0058] Figure 1 A flowchart of a high-resolution remote sensing image transmission method applied to mineral resource exploration is provided. The high-resolution remote sensing image transmission method applied to mineral resource exploration can be applied to a server. The high-resolution remote sensing image transmission method applied to mineral resource exploration can include the following S101 to S104.

[0059] S101, obtaining a high-resolution remote sensing image to be transmitted.

[0060] In this embodiment, the user first sets the geographic coordinates and the size of the shooting area according to the observation requirements. Then, the shooting time of the shooting area is set, such as sunny day, specific season or specific time period. Then, the corresponding resolution is set, including high resolution (less than 1 meter, used for city detail observation), medium resolution (10-30 meters, used for agriculture and ecological monitoring), and low resolution (more than 250 meters, used for global or regional scale monitoring).

[0061] Then, the server collects the high-resolution remote sensing image of the shooting area through a remote sensing satellite according to the geographic coordinates and the size of the shooting area, the shooting time of the shooting area and the corresponding resolution. And the collected high-resolution remote sensing image is preprocessed, including at least one of radiation correction, geometric correction and orthographic correction. The radiation correction is used to correct the sensor error and atmospheric influence, the geometric correction is used to eliminate the geometric distortion of the image, and the orthographic correction is used to project the image to the geographic coordinate system.

[0062] S102, convert the high-resolution remote sensing image from a spatial domain to a frequency domain by a discrete wavelet transform to obtain a remote sensing frequency image corresponding to the high-resolution remote sensing image.

[0063] In this embodiment, the discrete wavelet transform (DWT) is a discrete form of the continuous wavelet transform, which decomposes and reconstructs the signal through filters (low-pass filter and high-pass filter) at multiple levels to extract the local features of the signal at different scales.

[0064] The remote sensing frequency image is used to represent the image obtained after the discrete wavelet transform of the high-resolution remote sensing image.

[0065] As an example, the server uses the discrete wavelet transform algorithm to decompose the high-resolution remote sensing image to obtain the remote sensing frequency image corresponding to the high-resolution remote sensing image. Among them, the remote sensing frequency image includes high-frequency image and low-frequency image, and the low-frequency image contains the background information of the image, while the high-frequency image contains the edge and detail information of the image.

[0066] S103, set the blur weight of each image region according to the pixel density of each image region in the high-frequency image of the remote sensing frequency image.

[0067] In this embodiment, the blur weight is the weight corresponding to the image region. Among them, the region with higher pixel density (i.e. the region with rich details) can be given a lower blur weight to protect the edge information of these regions; while the region with lower pixel density (i.e. the smooth region) can be given a higher blur weight so that it can be more easily blurred during compression.

[0068] As an example, the server segments the high-frequency image in the remote sensing frequency image to obtain multiple image regions. Then, for each image region, calculate the pixel density of the image region. Then, according to the pixel density of each image region, set the blur weight corresponding to each image region. Specifically, the blur weight can be determined by an empirical formula or a machine learning model to ensure that the weight setting is reasonable and effective.

[0069] S104, compress the high-resolution remote sensing image according to the blur weight of each image region to obtain a compressed remote sensing image, so as to transmit the compressed remote sensing image.

[0070] In this embodiment, the compressed remote sensing image is used to represent the image obtained after the compression of the high-resolution remote sensing image.

[0071] As an example, the server uses a compression algorithm based on blur weight to compress high-resolution remote sensing images. This compression algorithm can compress different regions of the high-resolution remote sensing image to different degrees according to the different blur weights. During compression, more intense compression is performed on regions with higher blur weights (i.e., smooth regions) to reduce data volume; and weaker compression is performed on regions with lower blur weights (i.e., regions rich in details) to protect edge and detail information.

[0072] Then, the compressed remote sensing image can be stored and transmitted through a standard compression format (such as JPEG2000, PNG, etc.), and dedicated data transmission protocols and hardware can be used during transmission to ensure data integrity and security.

[0073] The application of the high-resolution remote sensing image transmission method for mineral resource exploration provided in the embodiment first converts the high-resolution remote sensing image from the spatial domain to the frequency domain through discrete wavelet transform to obtain a remote sensing frequency image corresponding to the high-resolution remote sensing image. Then, the weights of the image regions are set according to the pixel point density of each image region in the high-frequency image of the remote sensing frequency image. In this way, the discrete wavelet transform can identify the region of interest in the high-resolution remote sensing image. And according to the difference in pixel point density between the image regions of the region of interest, the weights of the image regions are set to ensure that the information of the region of interest is as complete as possible. Finally, the high-resolution remote sensing image is compressed according to the blur weight of the image region. In this way, by accurately identifying the region of interest and retaining the information of the region of interest as completely as possible, the compression effect of the remote sensing image can be improved.

[0074] As an optional embodiment, as shown in Figure 2 S103 can specifically include the following S201 to S205:

[0075] S201, clustering each pixel point according to the neighborhood density of each pixel point in the high-frequency image of the remote sensing frequency image to obtain a plurality of image regions;

[0076] S202, determining the region performance value of each image region according to the neighborhood density of each pixel point in each image region, the region performance value being used to represent how much high-frequency information is contained in the image region;

[0077] S203, determining the region attention degree of each image region according to the difference between the region performance value of each image region and the region performance value of the corresponding neighborhood region;

[0078] S204, determining the blur degree between each image region according to the difference in region attention degree and the difference in gray value between each image region;

[0079] S205, set the blur weight of each image region according to the blur degree between each image region.

[0080] In this embodiment, neighborhood density is used to represent the density of pixel points within a predetermined range centered on a pixel point. For example, the predetermined range is usually a fixed size window, such as 3x3, 5x5, etc.

[0081] Region performance value is used to reflect how much high-frequency information is contained in the image region. Among them, the greater the pixel density in the image region, the greater the region performance value.

[0082] Region attention degree is used to reflect the importance of the image region. Among them, the greater the difference between the region performance value of the image region and the region performance value of the neighborhood region, the greater the region attention degree.

[0083] Blur degree is used to reflect the degree of blur processing in the compression process. Among them, the greater the difference between the region attention degrees, the smaller the blur degree.

[0084] Neighborhood region is used to represent other image regions within a predetermined range centered on an image region.

[0085] As an example, the server first extracts high-frequency images from remote sensing frequency images, i.e. images obtained by high-pass filter decomposition, which contain edge and detail information of high-resolution remote sensing images. At the same time, for each pixel point in the high-frequency image, the neighborhood density is calculated. Then, using clustering algorithms (such as K-means, DBSCAN, etc.), the pixel points are clustered according to the neighborhood density, forming multiple image regions.

[0086] Then, for each image region, the average value of the neighborhood density of all pixel points inside is calculated as the region performance value of the image region. At the same time, in order to facilitate comparison, the region performance values of all image regions can be normalized to make their value range between [0, 1].

[0087] Then, for each image region, the neighborhood region within its predetermined range is obtained. And calculate the difference between the region performance value of the image region and the region performance value of the corresponding neighborhood region. According to the difference value, the region attention degree of the image region is determined. For example, if the difference is large, it means that the image region and its neighborhood region have significant differences in high-frequency information, so it should be given higher region attention degree.

[0088] Then, for each pair of image regions, the difference of their region attention degrees and the difference of their gray values are calculated. Combined with the attention degree difference and the gray value difference, a weighted algorithm is used to determine the blur degree between the pair of image regions. Among them, blur degree is used to reflect the smoothness of the transition between image regions.

[0089] Finally, a blur weight is assigned to each image region according to the blur degree between image regions. The blur weight can be linearly or non-linearly transformed based on the size of the blur degree to reflect the influence of the blur degree on subsequent processing.

[0090] Through the embodiment, the region performance value of each image region is determined according to the neighborhood density of each pixel point in each image region in the high-frequency image of the remote sensing frequency image; the region attention degree of each image region is determined according to the difference between the region performance value of each image region and the region performance value of the corresponding neighborhood region; the blur degree between image regions is determined according to the difference in region attention degrees and the difference in gray values between image regions; and finally, the blur weight of each image region is set according to the blur degree between image regions. In this way, by accurately setting the blur weight of each image region, the information of the region of interest can be ensured to be retained as completely as possible, thereby improving the compression effect of the remote sensing image.

[0091] As an optional embodiment, S201 can specifically include:

[0092] In each remote sensing frequency image, a high-frequency image is selected;

[0093] In the high-frequency image, the neighborhood density of each pixel point is calculated with the pixel point as the center;

[0094] According to the neighborhood density of each pixel point, each pixel point is clustered to obtain a plurality of image regions.

[0095] In the embodiment, the server first uses discrete wavelet transform to perform frequency decomposition on the high-resolution remote sensing image to obtain a plurality of remote sensing frequency images. Then, the high-frequency image obtained by the high-pass filter from the decomposed remote sensing frequency images is extracted.

[0096] Then, a neighborhood range is defined for each pixel point in the high-frequency image. The neighborhood range is usually a rectangular window (such as 3x3, 5x5, etc.) centered on the current pixel point, but can also be other shapes (such as a circle, an ellipse, etc.). For each pixel point in the high-frequency image, the statistical quantity (such as the mean, the median, the mode, etc.) of the pixel values in the neighborhood range is calculated as the neighborhood density of the pixel point. In addition, the number of non-zero pixel points in the neighborhood range or the number of pixel points above a certain threshold can also be used as the neighborhood density.

[0097] Finally, a suitable clustering algorithm is selected according to the characteristics and requirements of the high-resolution remote sensing image. Common clustering algorithms include K-means clustering, hierarchical clustering, DBSCAN, etc. Among them, K-means clustering is suitable for cases where data is evenly distributed, while DBSCAN has strong robustness to noise and outliers. For the selected clustering algorithm, relevant parameters (such as the number of clusters K, neighborhood radius ε, minimum number of sample points MinPts, etc.) are set. Then, according to the neighborhood density of each pixel point, the pixel points are clustered to form multiple image regions.

[0098] Through this embodiment, high-frequency images are selected from remote sensing frequency images, and clustering operations are performed according to the neighborhood density of pixel points, thereby obtaining multiple image regions with similar high-frequency characteristics. In this way, the high-frequency images are accurately divided into multiple image regions, which helps to set corresponding fuzzy weights for each image region in the subsequent process, thereby improving the compression effect of remote sensing images.

[0099] As an optional embodiment, S202 can specifically include:

[0100] For each image region, the following steps are performed:

[0101] The number of target pixel points of the target image region and the maximum number of pixel points in each image region are obtained, and the target image region is any one image region;

[0102] The neighborhood density of each pixel point in the target image region is calculated to obtain the pixel point density of the target image region;

[0103] The pixel point density, the number of target pixel points, and the maximum number of pixel points of the target image region are used to determine the region performance value of the target image region.

[0104] In this embodiment, the region performance value can be determined by the following formula 1:

[0105] Formula 1

[0106] In formula 1, is used to represent the region performance value of the jth image region, is used to represent the number of pixel points of the jth image region, is used to represent the maximum number of pixel points in each image region of the high-frequency image, is used to represent the preset non-zero coefficient, which can be specifically 0.1, is used to represent the neighborhood density of the ith pixel point of the jth image region.

[0107] Among them, the pixel density of the jth image region is higher, the more high-frequency information the jth image region contains, and thus the larger the region performance value of the jth image region is; a difference between the number of pixel points in the jth image region and the maximum number of pixel points in each image region, the larger the difference is, the less high-frequency information the jth image region contains, and thus the smaller the region performance value of the jth image region is.

[0108] the region performance value of the jth image region the larger the region performance value of the jth image region is, the more high-frequency information the jth image region contains, that is, the more obvious the boundary between the region of interest and the background region in the jth image region is.

[0109] Through the embodiment, the pixel density of the target image region, the number of target pixel points of the target image region, and the maximum number of pixel points in each image region are used to accurately determine the region performance value of the target image region. In this way, it is helpful to accurately determine the blur weight of the target image region according to the region performance value, thereby improving the compression effect of the remote sensing image.

[0110] As an optional embodiment, S203 can specifically include:

[0111] For each image region, the following steps are performed respectively:

[0112] obtaining the Euclidean distance between the target image region and each corresponding neighborhood region, the target image region being any one image region;

[0113] obtaining a plurality of region performance differences by taking the absolute value of the difference between the region performance value of the target image region and the region performance value of each neighborhood region;

[0114] determining the region attention degree of the target image region by using each Euclidean distance and each region performance difference.

[0115] In the embodiment, the neighborhood region of the target image region is other image regions contained in the circumference of a circle with the center point of the target image region as the origin and a preset radius.

[0116] As an example, the region attention degree can be determined by the following formula 2:

[0117] Formula 2

[0118] In formula 2, to represent the region attention degree of the jth image region, to represent the region performance value of the jth image region, to represent the region performance value of the rth neighborhood region, is used to represent the Euclidean distance between the jth image region and the rth neighborhood region. n is used to represent the number of neighborhood regions, is used to represent the linear normalization function.

[0119] wherein, is used to represent the region performance difference between the jth image region and the rth neighborhood region. The greater the region performance difference, the more prominent the jth image region, and the greater the region attention of the jth image region. The smaller the Euclidean distance between the jth image region and the rth neighborhood region, the more prominent the jth image region, and the greater the region attention of the jth image region.

[0120] Through the embodiment, the Euclidean distance between the target image region and the corresponding each neighborhood region, and the region performance difference of the target image region are used to accurately determine the region attention of the target image region. In this way, it is helpful to accurately determine the blur weight of the target image region according to the region attention, thereby improving the compression effect of the remote sensing image.

[0121] As an optional embodiment, S204 can specifically include:

[0122] The gray mean value of the first image region and the gray mean value of the second image region are obtained, and the first image region and the second image region are any two different image regions in each image region;

[0123] The gray mean value of the first image region is subtracted from the gray mean value of the second image region to obtain the gray performance difference;

[0124] The region attention of the first image region is subtracted from the region attention of the second image region to obtain the attention performance difference;

[0125] The blur degree between the first image region and the second image region is determined by using the gray performance difference and the attention performance difference.

[0126] In the embodiment, the blur degree can be determined by the following formula 3:

[0127] Formula 3

[0128] In formula 3, is used to represent the blur degree between the jth image region and the fth image region, is used to represent the gray mean value of the jth image region in the high-resolution remote sensing image, is used to represent the gray mean value of the fth image region in the high-resolution remote sensing image. is used to represent the region attention of the jth image region in the lth high-frequency image, a region attention degree for characterizing the jth image region in the lth high-frequency image, m is used to represent the number of high-frequency images, an exponential function with a natural constant as a base.

[0129] wherein, a gray level performance difference between the jth image region and the fth image region, the smaller the gray level performance difference, the easier the decomposition positions of the two image regions to be adhered, that is, the greater the edge blurring degree, that is, the greater the blurring degree; a region attention performance difference between the jth image region and the fth image region in the lth high-frequency image, the smaller the region attention performance difference, the smaller the high-frequency information difference, that is, the smaller the blurring degree.

[0130] Through the embodiment, the blurring degree between the first image region and the second image region is accurately determined by using the gray level performance difference between the first image region and the second image region and the region attention performance difference between the first image region and the second image region. In this way, it is helpful to accurately determine the blurring weight of the image region according to the blurring degree, thereby improving the compression effect of the remote sensing image.

[0131] As an optional embodiment, S205 can specifically include:

[0132] For each image region, the following steps are performed respectively:

[0133] obtaining the plane distances between the target image regions in each high-frequency image, the target image region being any one image region;

[0134] determining the blurring weight of the target image region by using the blurring degrees between the target image region and each neighboring region and each plane distance.

[0135] In the embodiment, the blurring weight can be determined by the following formula 4:

[0136] Formula 4

[0137] In formula 4, a blurring weight of the jth image region, a blurring degree between the jth image region and the rth neighboring region, n is used to represent the number of neighboring regions. a plane distance between the centroid point of the jth image region in the lth high-frequency image and the centroid point of the jth image region in the cth high-frequency image, m is used to represent the number of high-frequency images.

[0138] The greater the blur degree between the jth image area and the adjacent area, the greater the blur weight of the jth image area; the greater the planar distance between the jth image area in each high-frequency image, the greater the blur weight of the jth image area.

[0139] Through the embodiment, the blur weight of the target image area is accurately determined by using the blur degree between the target image area and each adjacent area and the planar distance between the target image area in each high-frequency image, so that the compression effect of the remote sensing image can be improved.

[0140] The application discloses a high-resolution remote sensing image transmission method applied to mineral resource exploration.

[0141] Figure 3 A structural schematic diagram of a high-resolution remote sensing image transmission system applied to mineral resource exploration is provided, and the high-resolution remote sensing image transmission system 400 applied to mineral resource exploration comprises an image acquisition module 410, an image conversion module 420, a weight setting module 430 and an image compression module 440.

[0142] The image acquisition module 410 is used for acquiring a high-resolution remote sensing image to be transmitted.

[0143] The image conversion module 420 is used for converting the high-resolution remote sensing image from a spatial domain to a frequency domain by discrete wavelet transform to obtain a remote sensing frequency image corresponding to the high-resolution remote sensing image.

[0144] The weight setting module 430 is used for setting a blur weight of each image area according to a pixel point density of each image area in a high-frequency image of the remote sensing frequency image.

[0145] The image compression module 440 is used for compressing the high-resolution remote sensing image according to the blur weight of each image area by using a compression algorithm based on the blur weight to obtain a compressed remote sensing image, so that the compressed remote sensing image can be transmitted.

[0146] The application provided by the embodiment applied to the high-resolution remote sensing image transmission system for mineral resource exploration, first, the high-resolution remote sensing image is converted from the spatial domain to the frequency domain through the discrete wavelet transform, and the remote sensing frequency image corresponding to the high-resolution remote sensing image is obtained. Then, according to the pixel point density of each image area in the high-frequency image of the remote sensing frequency image, the weight of each image area is set. In this way, through the discrete wavelet transform, the region of interest in the high-resolution remote sensing image can be identified. And according to the difference in pixel point density between each image area of the region of interest, the weight of each image area is set, which can ensure that the information of the region of interest is retained as completely as possible. Finally, according to the fuzzy weight of the image area, the high-resolution remote sensing image is compressed. In this way, by accurately identifying the region of interest and retaining the information of the region of interest as completely as possible, the compression effect of the remote sensing image can be improved.

[0147] It should be noted that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of well-known methods are omitted herein. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order of the steps, after understanding the spirit of the present application.

[0148] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiments, or in an order different from the embodiments, or several steps can be performed simultaneously.

[0149] The above is only a specific implementation of the present application, and those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, module and unit can refer to the corresponding process in the foregoing method embodiments, which will not be described here. It should be understood that the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A high-resolution remote sensing image transmission method applied to mineral resource exploration, characterized in that, The method comprises: acquiring a high-resolution remote sensing image to be transmitted; converting the high-resolution remote sensing image from a spatial domain to a frequency domain through discrete wavelet transform to obtain a remote sensing frequency image corresponding to the high-resolution remote sensing image; setting a blur weight of each image region according to a pixel point density of each image region in a high-frequency image of the remote sensing frequency image; compressing the high-resolution remote sensing image according to the blur weight of each image region using a compression algorithm based on the blur weight to obtain a compressed remote sensing image, so that the compressed remote sensing image is transmitted; the setting method of the blur weight of each image region comprises: clustering each pixel point according to a neighborhood density of each pixel point in a high-frequency image of the remote sensing frequency image to obtain a plurality of image regions; determining a region performance value of each image region according to the neighborhood density of each pixel point in each image region, the region performance value being used to represent how much high-frequency information is contained in the image region; determining a region attention degree of each image region according to a difference between the region performance value of each image region and a region performance value of a corresponding neighborhood region; determining a blur degree between each image region according to a difference between the region attention degrees and a difference between gray values of each image region; setting the blur weight of each image region according to the blur degree between each image region.

2. The high-resolution remote sensing image transmission method for mineral resource exploration according to claim 1, characterized in that, The clustering of each pixel point according to the neighborhood density of each pixel point in the high-frequency image of the remote sensing frequency image to obtain a plurality of image regions comprises: filtering out the high-frequency image in each remote sensing frequency image; calculating a neighborhood density of each pixel point in the high-frequency image with each pixel point as a center; clustering each pixel point according to the neighborhood density of each pixel point to obtain a plurality of image regions.

3. The high-resolution remote sensing image transmission method for mineral resource exploration according to claim 1, characterized in that, The determination of the region performance value of each image region according to the neighborhood density of each pixel point in each image region comprises: the following steps are performed for each image region respectively: acquiring a target pixel point quantity of a target image region and a maximum pixel point quantity in each image region, the target image region being any one of the image regions; performing mean value calculation on the neighborhood density of each pixel point in the target image region to obtain a pixel point density of the target image region; determining the region performance value of the target image region by using the pixel point density of the target image region, the target pixel point quantity and the maximum pixel point quantity.

4. The high-resolution remote sensing image transmission method for mineral resource exploration according to claim 1, characterized in that, The determination of the region attention degree of each image region according to a difference between the region performance value of each image region and a region performance value of a corresponding neighborhood region comprises: the following steps are performed for each image region respectively: acquiring a Euclidean distance between a target image region and each neighborhood region corresponding to the target image region, the target image region being any one of the image regions; obtaining a plurality of region performance differences by taking an absolute value of a difference between the region performance value of the target image region and the region performance value of each neighborhood region; The region attention degrees of the target image regions are determined by using the Euclidean distances and the region performance differences.

5. The high-resolution remote sensing image transmission method for mineral resource exploration according to claim 1, characterized in that, The blur degrees between the image regions are determined according to the difference between the region attention degrees and the difference between the gray values. The gray mean value of a first image region and the gray mean value of a second image region are obtained, the first image region and the second image region being any two different image regions among the image regions; The gray performance difference is obtained by taking the absolute value of the difference between the gray mean value of the first image region and the gray mean value of the second image region. The attention performance difference is obtained by taking the absolute value of the difference between the region attention degree of the first image region and the region attention degree of the second image region. The blur degree between the first image region and the second image region is determined by using the gray performance difference and the attention performance difference. 6.The high-resolution remote sensing image transmission method for mineral resource exploration according to claim 1, characterized in that, The blur weights of the image regions are set according to the blur degrees between the image regions. The following steps are performed for each image region: The plane distances between target image regions in each high-frequency image are obtained, the target image regions being any one of the image regions; The blur weights of the target image regions are determined by using the blur degrees between the target image regions and the neighborhood regions and the plane distances.

7. A high-resolution remote sensing image transmission system applied to mineral resource exploration, characterized in that, The system comprises: An image acquisition module is configured to acquire high-resolution remote sensing images to be transmitted. An image conversion module is configured to convert the high-resolution remote sensing images from a spatial domain to a frequency domain by discrete wavelet transform to obtain remote sensing frequency images corresponding to the high-resolution remote sensing images. A weight setting module is configured to set blur weights of image regions in high-frequency images of the remote sensing frequency images according to pixel point densities of the image regions. An image compression module is configured to compress the high-resolution remote sensing images according to the blur weights of the image regions by using a compression algorithm based on the blur weights to obtain compressed remote sensing images, so that the compressed remote sensing images are transmitted. The blur weights of the image regions are set by: Clustering each pixel point in a high-frequency image of the remote sensing frequency images according to a neighborhood density of the pixel point to obtain a plurality of image regions; Determining region performance values of the image regions according to neighborhood densities of the pixel points in the image regions, the region performance values being used to represent how much high-frequency information is contained in the image regions; Determining region attention degrees of the image regions according to differences between the region performance values of the image regions and region performance values of corresponding neighborhood regions; Determining blur degrees between the image regions according to the difference between the region attention degrees and the difference between the gray values; Setting blur weights of the image regions according to the blur degrees between the image regions.

Images