CN121056637A - High-resolution remote sensing image transmission method and system applied to mineral resource exploration - Google Patents

Abstract

The invention discloses a high-resolution remote sensing image transmission method and a system applied to mineral resource exploration, and relates to the technical field of image processing. The method comprises the steps of obtaining high-resolution remote sensing images to be transmitted, converting the high-resolution remote sensing images from a space domain to a frequency domain through discrete wavelet transformation to obtain remote sensing frequency images corresponding to the high-resolution remote sensing images, setting fuzzy weights of the image areas according to pixel point densities of the image areas in the high-frequency images of the remote sensing frequency images, wherein the fuzzy weights are used for representing weights of the image areas in an edge fuzzy function, and compressing the high-resolution remote sensing images according to the fuzzy weights of the image areas to obtain compressed remote sensing images so as to transmit the compressed remote sensing images. The high-resolution remote sensing image transmission method applied to mineral resource exploration can improve the compression effect of remote sensing images.

Description

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

Technical Field

This invention relates to the field of image processing technology, and specifically to a high-resolution remote sensing image transmission method and system for mineral resource exploration.

Background Technology

With the rapid development of remote sensing technology, high-resolution remote sensing imagery is increasingly being used in mineral resource exploration. However, high-resolution remote sensing images involve massive amounts of data and have high transmission requirements, and traditional remote sensing image transmission methods have many limitations.

In existing methods, the JPEG2000 image compression algorithm is used to compress high-resolution remote sensing images before the compressed images are transmitted.

However, remote sensing images are typically multispectral images, containing multiple bands. The boundary between the region of interest and the background region is often blurred, making it difficult to accurately identify the region of interest and the background region, resulting in poor compression of the remote sensing image.

Summary of the Invention

This invention provides a high-resolution remote sensing image transmission method and system for mineral resource exploration, which can improve the compression effect of remote sensing images.

A first aspect of this invention provides a high-resolution remote sensing image transmission method for mineral resource exploration, comprising:

Acquire high-resolution remote sensing images to be transmitted;

By using discrete wavelet transform, high-resolution remote sensing images are transformed from the spatial domain to the frequency domain to obtain the remote sensing frequency images corresponding to the high-resolution remote sensing images.

Based on the pixel density of each image region in the high-frequency image of the remote sensing frequency image, a blur weight is set for each image region. The blur weight is used to characterize the weight of the image region in the edge blur function.

Based on the fuzzy weights of each image region, the high-resolution remote sensing image is compressed to obtain a compressed remote sensing image, which is then transmitted.

Further, the step of setting the blur weight of each image region based on the pixel density of each image region in the high-frequency image of the remote sensing frequency image includes:

Based on the neighborhood density of each pixel in the high-frequency image of the remote sensing frequency image, the pixels are clustered to obtain multiple image regions.

Based on the neighborhood density of each pixel in each image region, a regional performance value is determined for each image region, and the regional performance value is used to characterize the amount of high-frequency information contained in the image region.

The regional attention of each image region is determined based on the difference between the regional performance value of each image region and the regional performance value of the corresponding neighboring region.

The blurring degree between each image region is determined based on the difference in regional attention and the difference in grayscale value between each image region.

Based on the blurriness between the image regions, a blur weight is set for each image region.

Further, based on the neighborhood density of each pixel in the high-frequency image of the remote sensing frequency image, the pixels are clustered to obtain multiple image regions, including:

The high-frequency images are selected from the remote sensing frequency images.

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

Based on the neighborhood density of each pixel, the pixels are clustered to obtain multiple image regions.

Further, determining the regional representation value of each image region based on the neighborhood density of each pixel in each image region includes:

For each of the aforementioned image regions, the following steps are performed:

The number of target pixels in the target image region and the maximum number of pixels in each of the image regions are obtained, wherein the target image region is any one of the image regions;

The pixel density of the target image region is obtained by calculating the mean of the neighborhood density of each pixel in the target image region.

The region performance value of the target image region is determined by using the pixel density of the target image region, the number of target pixels, and the maximum number of pixels.

Further, determining the regional attention level of each image region based on the difference between the regional performance value of each image region and the regional performance value of its corresponding neighboring region includes:

For each of the aforementioned image regions, the following steps are performed:

Obtain the Euclidean distance between the target image region and its corresponding neighboring regions, wherein the target image region is any one of the image regions;

The absolute value of the difference between the regional performance value of the target image region and the regional performance value of each of the neighboring regions is taken to obtain multiple regional performance differences.

The region attention of the target image region is determined by utilizing the Euclidean distances and the differences in region performance.

Further, determining the blurriness between each of the image regions based on the difference in regional attention and the difference in grayscale values includes:

The average grayscale value of a first image region and the average grayscale value of a second image region are obtained, wherein the first image region and the second image region are any two different image regions in each of the image regions;

The difference between the mean grayscale value of the first image region and the mean grayscale value of the second image region is taken as the absolute value to obtain the grayscale performance difference.

The difference between the regional attention of the first image region and the regional attention of the second image region is taken as the absolute value to obtain the difference in attention performance.

The blurring between the first image region and the second image region is determined by using the differences in grayscale representation and the differences in attention representation.

Further, setting the blur weight of each image region based on the blur degree between each image region includes:

For each of the aforementioned image regions, the following steps are performed:

Obtain the planar distance between target image regions in each high-frequency image, wherein the target image region is any one of the image regions;

The blur weight of the target image region is determined by using the blur degree between the target image region and each of the neighboring regions and the planar distances.

Further, the step of compressing the high-resolution remote sensing image according to the fuzzy weights of each of the image regions to obtain a compressed remote sensing image includes:

Based on the blur weights of each image region, a blur function is created for each image region;

The optimal solutions for each of the fuzzy functions are obtained using the gradient descent method.

Based on the optimal solution of each of the aforementioned fuzzy functions, determine the edge constraint coefficient of each of the aforementioned image regions;

The high-resolution remote sensing image is compressed according to the edge limiting coefficients to obtain the compressed remote sensing image.

Further, determining the edge constraint coefficient of each image region based on the optimal solution of each of the blur functions includes:

Obtain the maximum and minimum values of the fuzzy function;

Subtracting the minimum value from the maximum value of the function yields the range of the fuzzy function.

The edge constraint coefficient of the image region is determined by using the optimal solution of the fuzzy function and the function range of the fuzzy function.

A second aspect of the present invention provides a high-resolution remote sensing image transmission system for mineral resource exploration, comprising:

The image acquisition module is used to acquire high-resolution remote sensing images to be transmitted.

The image conversion module is used to convert high-resolution remote sensing images from the spatial domain to the frequency domain through discrete wavelet transform, so as to obtain the remote sensing frequency image corresponding to the high-resolution remote sensing image.

The weight setting module is used to set the blur weight of each image region based on the pixel density of each image region in the high-frequency image of the remote sensing frequency image. The blur weight is used to characterize the weight of the image region in the edge blur function.

The image compression module is used to compress high-resolution remote sensing images according to the blur weights of each image region to obtain compressed remote sensing images for transmission.

The high-resolution remote sensing image transmission method for mineral resource exploration provided in this invention first transforms the high-resolution remote sensing image from the spatial domain to the frequency domain using discrete wavelet transform, obtaining the corresponding remote sensing frequency image. Then, based on the pixel density of each image region in the high-frequency image of the remote sensing frequency image, the weights of each image region in the edge blurring function are set. Thus, through discrete wavelet transform, the region of interest (ROI) in the high-resolution remote sensing image can be identified. Furthermore, by setting the weights of each image region in the edge blurring function based on the differences in pixel density between different ROI regions, the information of the ROI can be preserved as completely as possible. Finally, the high-resolution remote sensing image is compressed based on the blur weights of the image regions. Therefore, by accurately identifying the ROI and preserving its information as completely as possible, the compression effect of the remote sensing image can be improved.

Attached Figure Description

To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

Figure 1 is a flowchart illustrating a first high-resolution remote sensing image transmission method for mineral resource exploration provided by an embodiment of the present invention.

Figure 2 is a flowchart illustrating a second high-resolution remote sensing image transmission method for mineral resource exploration provided in an embodiment of the present invention.

Figure 3 is a flowchart illustrating a third high-resolution remote sensing image transmission method for mineral resource exploration provided in an embodiment of the present invention.

Figure 4 is a schematic diagram of the structure of a high-resolution remote sensing image transmission system for mineral resource exploration provided by an embodiment of the present invention.

Detailed Implementation

To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a high-resolution remote sensing image transmission method and system for mineral resource exploration proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

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 this invention pertains.

It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this invention all comply with the relevant provisions of laws and regulations.

It should be noted that in the embodiments of the present invention, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of the present invention. However, they do not mean that the applicant has used or necessarily used the solution.

With the rapid development of remote sensing technology, high-resolution remote sensing imagery is increasingly being used in mineral resource exploration. However, high-resolution remote sensing images involve massive amounts of data and have high transmission requirements, and traditional remote sensing image transmission methods have many limitations.

Existing methods use the JPEG2000 image compression algorithm to compress high-resolution remote sensing images before transmitting the compressed images. However, remote sensing images are typically multispectral images containing multiple bands. The boundary between the region of interest (ROI) and the background region is often blurred, making it difficult to accurately identify the ROI and resulting in poor compression performance.

The purpose of this invention is to provide a method and system for transmitting high-resolution remote sensing images applied to mineral resource exploration. The method for transmitting high-resolution remote sensing images for mineral resource exploration provided in this invention first uses discrete wavelet transform to convert the high-resolution remote sensing image from the spatial domain to the frequency domain, obtaining the corresponding remote sensing frequency image. Then, based on the pixel density of each image region in the high-frequency image of the remote sensing frequency image, the weights of each image region in the edge blurring function are set. Thus, through discrete wavelet transform, the region of interest (ROI) in the high-resolution remote sensing image can be identified. Furthermore, by setting the weights of each image region in the edge blurring function based on the differences in pixel density between different ROI regions, the information of the ROI can be preserved as completely as possible. Finally, the high-resolution remote sensing image is compressed according to the blurring weights of the image regions. Therefore, by accurately identifying the ROI and preserving its information as completely as possible, the compression effect of the remote sensing image can be improved.

The following describes specific embodiments of the high-resolution remote sensing image transmission method and system for mineral resource exploration provided by the present invention.

Figure 1 provides a flowchart of a high-resolution remote sensing image transmission method for mineral resource exploration. This high-resolution remote sensing image transmission method for mineral resource exploration can be applied to a server and may include the following steps S101 to S104.

S101: Acquire high-resolution remote sensing images to be transmitted.

In this embodiment, the user first sets the geographic coordinates and size of the shooting area according to the observation requirements; then sets the shooting time for the shooting area, such as sunny day, specific season, or specific time period; then sets the corresponding resolution, including high resolution (less than 1 meter, used for urban detail observation); medium resolution (10-30 meters, used for agricultural and ecological monitoring); and low resolution (greater than 250 meters, used for global or regional scale monitoring).

Then, the server acquires high-resolution remote sensing images of the captured area via remote sensing satellites, based on the geographical coordinates, size, capture time, and corresponding resolution of the area. The acquired high-resolution remote sensing images are then preprocessed, including at least one of radiometric correction, geometric correction, and orthorectification. Radiometric correction corrects sensor errors and atmospheric effects, geometric correction eliminates geometric distortions, and orthorectification projects the images onto a geographic coordinate system.

S102 uses discrete wavelet transform to convert the high-resolution remote sensing image from the spatial domain to the frequency domain, thus obtaining the remote sensing frequency image corresponding to the high-resolution remote sensing image.

In this embodiment, Discrete Wavelet Transform (DWT) is a discretized form of continuous wavelet transform. It decomposes and reconstructs the signal at multiple levels through filters (low-pass filter and high-pass filter) to extract local features of the signal at different scales.

Remote sensing frequency images are used to characterize images obtained by performing discrete wavelet transform on high-resolution remote sensing images.

As an example, the server uses the discrete wavelet transform algorithm to decompose the high-resolution remote sensing image into a corresponding remote sensing frequency image. This frequency image includes both high-frequency and low-frequency images. The low-frequency image contains background information, while the high-frequency image contains edge and detail information.

S103, based on the pixel density of each image region in the high-frequency image of the remote sensing frequency image, set the blur weight of each image region. The blur weight is used to characterize the weight of the image region in the edge blur function.

In this embodiment, the blur weight is the weight corresponding to the image region in the edge blur function. Regions with higher pixel density (i.e., regions rich in detail) can be assigned lower blur weights to preserve their edge information; while regions with lower pixel density (i.e., smooth regions) can be assigned higher blur weights so that they are more easily blurred during compression.

As an example, the server segments high-frequency images from remote sensing imagery into multiple image regions. Then, for each image region, its pixel density is calculated. Next, based on the pixel density of each image region, a blur weight is set for that region. Specifically, the blur weights can be determined using empirical formulas or machine learning models to ensure that the weight settings are reasonable and effective.

S104. Based on the fuzzy weights of each image region, the high-resolution remote sensing image is compressed to obtain a compressed remote sensing image, which is then transmitted.

In this embodiment, compressed remote sensing imagery is used to characterize the image obtained by compressing high-resolution remote sensing imagery.

As an example, the server uses a fuzzy weight-based compression algorithm to compress high-resolution remote sensing imagery. This algorithm can apply different degrees of compression to different regions of the high-resolution remote sensing imagery based on their fuzzy weights. During compression, regions with higher fuzzy weights (i.e., smooth regions) are compressed more strongly to reduce data volume, while regions with lower fuzzy weights (i.e., regions rich in detail) are compressed less strongly to preserve edge and detail information.

Then, the compressed remote sensing images can be stored and transmitted using standard compression formats (such as JPEG2000, PNG, etc.). During transmission, dedicated data transmission protocols and hardware can be used to ensure data integrity and security.

The high-resolution remote sensing image transmission method for mineral resource exploration provided in this embodiment first transforms the high-resolution remote sensing image from the spatial domain to the frequency domain using discrete wavelet transform, obtaining the corresponding remote sensing frequency image. Then, based on the pixel density of each image region in the high-frequency image of the remote sensing frequency image, the weights of each image region in the edge blurring function are set. Thus, through discrete wavelet transform, the region of interest (ROI) in the high-resolution remote sensing image can be identified. Furthermore, by setting the weights of each image region in the edge blurring function based on the differences in pixel density between different ROI regions, the information of the ROI can be preserved as completely as possible. Finally, the high-resolution remote sensing image is compressed based on the blur weights of the image regions. Therefore, by accurately identifying the ROI and preserving its information as completely as possible, the compression effect of the remote sensing image can be improved.

As an optional embodiment, as shown in FIG2, S103 may specifically include the following S201 to S205:

S201. Based on the neighborhood density of each pixel in the high-frequency image of the remote sensing frequency image, cluster each pixel to obtain multiple image regions.

S202, Based on the neighborhood density of each pixel in each image region, determine the regional performance value of each image region. The regional performance value is used to characterize the amount of high-frequency information contained in the image region.

S203, determine the regional attention of each image region based on the difference between the regional performance value of each image region and the regional performance value of the corresponding neighboring region;

S204. Determine the blur between each image region based on the difference in regional attention and the difference in grayscale values between each image region.

S205, set the blur weight of each image region according to the blur between each image region.

In this embodiment, neighborhood density is used to characterize the pixel density within a preset range centered on a pixel. For example, the preset range is typically a window of a fixed size, such as 3x3, 5x5, etc.

The region performance value reflects the amount of high-frequency information contained in an image region. The higher the pixel density in an image region, the higher the region performance value.

Regional attention is used to reflect the importance of an image region. The greater the difference between the regional performance value of an image region and the regional performance value of its neighboring regions, the greater the regional attention.

Ambiguity refers to the degree of blurring during compression. The greater the difference between areas of interest, the smaller the ambiguity.

The neighboring region is used to represent other image regions within a preset range centered on the image region.

As an example, the server first extracts high-frequency imagery from remote sensing imagery, specifically the imagery obtained by decomposing it using a high-pass filter, which contains edge and detail information from the high-resolution remote sensing imagery. Simultaneously, for each pixel in the high-frequency imagery, its neighborhood density is calculated. Then, clustering algorithms (such as K-means, DBSCAN, etc.) are used to cluster the pixels based on their neighborhood density, forming multiple image regions.

Then, for each image region, the average neighborhood density of all pixels within it is calculated and used as the region representation value of that image region. Simultaneously, for ease of comparison, the region representation values of all image regions can be normalized so that their values are within the range [0,1].

Then, for each image region, its neighboring regions within a preset range are obtained. The difference between the regional performance value of the image region and the corresponding regional performance values of its neighboring regions is calculated. Based on the magnitude of the difference, the regional attention level of the image region is determined. For example, if the difference is large, it indicates a significant difference in high-frequency information between the image region and its neighboring regions, and therefore a higher regional attention level should be given.

Then, for each pair of image regions, the difference in their regional attention and the difference in their grayscale values are calculated. Combining the attention difference and grayscale value difference, a weighted algorithm is used to determine the blurriness between the pair of image regions. The blurriness reflects the smoothness of the transition between image regions.

Finally, a blur weight is assigned to each image region based on the degree of blur between them. The blur weight can be transformed linearly or nonlinearly based on the magnitude of blur to reflect the impact of the blur level on subsequent processing.

In this embodiment, the regional representation value of each image region is determined based on the neighborhood density of each pixel in each image region of the high-frequency imagery. Then, the regional attention of each image region is determined based on the difference between its regional representation value and the regional representation values of its corresponding neighboring regions. Next, the blurriness between image regions is determined based on the difference in regional attention and the difference in grayscale values. Finally, the blur weight of each image region is set based on its blurriness. Thus, by accurately setting the blur weight of each image region in the edge blur function, the information of the region of interest can be preserved as completely as possible, thereby improving the compression effect of the remote sensing imagery.

As an optional embodiment, S201 may specifically include:

High-frequency images were selected from the remote sensing images at various frequencies.

In high-frequency images, the neighborhood density of each pixel is calculated with each pixel as the center.

Based on the neighborhood density of each pixel, the pixels are clustered to obtain multiple image regions.

In this embodiment, the server first uses discrete wavelet transform to perform frequency decomposition on the high-resolution remote sensing image, obtaining multiple remote sensing frequency images. Then, from each decomposed remote sensing frequency image, the high-frequency image obtained by decomposition through a high-pass filter is extracted.

Next, a neighborhood range is defined for each pixel in the high-frequency image. This neighborhood range is typically a rectangular window centered on the current pixel (e.g., 3x3, 5x5, etc.), but can also be other shapes (e.g., circle, ellipse, etc.). For each pixel in the high-frequency image, a statistical measure (e.g., mean, median, mode, etc.) of the pixel values within its neighborhood is calculated as the neighborhood density for that pixel. Alternatively, the number of non-zero pixels within the neighborhood or the number of pixels above a specific threshold can also be used as the neighborhood density.

Finally, a suitable clustering algorithm is selected based on the characteristics and requirements of the high-resolution remote sensing image. Commonly used clustering algorithms include K-means clustering, hierarchical clustering, and DBSCAN. Among them, K-means clustering is suitable for situations where the data distribution is relatively uniform, while DBSCAN has strong robustness to noise and outliers. For the selected clustering algorithm, relevant parameters are set (such as the number of clusters K, neighborhood radius ε, minimum number of sample points MinPts, etc.). Then, based on the neighborhood density of each pixel, the pixels are clustered to form multiple image regions.

This embodiment filters high-frequency images from remote sensing imagery and performs clustering based on pixel neighborhood density to obtain multiple image regions with similar high-frequency characteristics. This accurately divides the high-frequency images into multiple image regions, facilitating the subsequent setting of corresponding blur weights for each region and thus improving the compression effect of the remote sensing images.

As an optional embodiment, S202 may specifically include:

For each image region, perform the following steps:

Obtain the number of target pixels in the target image region and the maximum number of pixels in each image region. The target image region can be any image region.

The pixel density of the target image region is obtained by calculating the mean of the neighborhood density of each pixel in the target image region.

The regional performance value of the target image region is determined by using the pixel density, the number of target pixels, and the maximum number of pixels in the target image region.

In this embodiment, the regional performance value can be specifically determined using the following formula 1:

Formula 1

In formula 1, The regional performance value used to characterize the j-th image region. Used to represent the number of pixels in the j-th image region The maximum number of pixels in each image region used to characterize high-frequency images. The non-zero coefficient used to characterize the preset value can optionally be 0.1. Used to characterize the neighborhood density of the i-th pixel in the j-th image region.

in, The pixel density of the j-th image region is used to characterize the pixel density of the j-th image region. The higher the pixel density of the j-th image region, the more high-frequency information it contains, and therefore the larger the regional performance value of the j-th image region. This value is used to characterize the difference between the number of pixels in the j-th image region and the maximum number of pixels in each image region. The larger the difference, the less high-frequency information is contained in the j-th image region, and therefore the smaller the regional performance value of the j-th image region.

The regional performance value of the j-th image region The larger the value, the more high-frequency information is contained in the j-th image region, meaning the more distinct the boundary between the region of interest and the background region is in the j-th image region.

This embodiment utilizes the pixel density, the number of target pixels in the target image region, and the maximum number of pixels in each image region to accurately determine the regional performance value of the target image region. This helps to accurately determine the blur weight of the target image region in the edge blur function based on the regional performance value, thereby improving the compression effect of remote sensing images.

As an optional embodiment, S203 may specifically include:

For each image region, perform the following steps:

Obtain the Euclidean distance between the target image region and its corresponding neighboring regions. The target image region can be any image region.

The absolute value of the difference between the regional performance value of the target image region and the regional performance values of each neighboring region is taken to obtain the regional performance differences.

By utilizing the differences in Euclidean distances and regional performance, the regional attention of the target image area is determined.

In this embodiment, the neighborhood region of the target image region is the other image regions contained within a circle with a preset radius, with the center point of the target image region as the origin.

As an example, regional attention can be specifically determined using the following formula 2:

Formula 2

In formula 2, Used to characterize the regional attention of the j-th image region The regional performance value used to characterize the j-th image region. The regional performance value used to characterize the r-th neighborhood region The distance is used to represent the Euclidean distance between the j-th image region and its r-th neighboring region. n represents the number of neighboring regions. Used to characterize linear normalization functions.

in, The difference in regional performance between the j-th image region and its r-th neighboring region is used to characterize the difference in regional performance. The greater the difference in regional performance, the more prominent the j-th image region is, and the greater its regional attention. The Euclidean distance between the j-th image region and its r-th neighboring region is also considered. The smaller the value, the more prominent the j-th image region is, and the greater its regional attention.

This embodiment utilizes the Euclidean distance between the target image region and its corresponding neighboring regions, as well as the differences in the appearance of different regions within the target image region, to accurately determine the regional attention level of the target image region. This helps in subsequently determining the blur weight of the target image region in the edge blurring function based on the regional attention level, thereby improving the compression effect of remote sensing images.

As an optional embodiment, S204 may specifically include:

Obtain the average grayscale value of the first image region and the average grayscale value of the second image region, wherein the first image region and the second image region are any two different image regions in each image region;

The difference between the mean grayscale value of the first image region and the mean grayscale value of the second image region is taken as the absolute value to obtain the difference in grayscale performance.

The difference between the regional attention of the first image region and the regional attention of the second image region is taken as the absolute value to obtain the difference in attention performance.

The blurring between the first image region and the second image region is determined by utilizing the differences in grayscale representation and attention representation.

In this embodiment, the ambiguity can be specifically determined using the following formula 3:

Formula 3

In formula 3, Used to characterize the blur between the j-th image region and the f-th image region. Used to characterize the grayscale mean of the j-th image region in a high-resolution remote sensing image. Used to characterize the grayscale mean of the f-th image region in a high-resolution remote sensing image. Used to characterize the regional attention of the j-th image region in the l-th high-frequency image. The region of interest is used to characterize the f-th image region in the l-th high-frequency image, and m is used to characterize the number of high-frequency images. Used to characterize exponential functions with the natural constant as the base.

in, This is used to characterize the difference in grayscale performance between the j-th image region and the f-th image region. The smaller the difference in grayscale performance, the easier it is for the decomposition positions of the two image regions to stick together, which means the greater the degree of edge blurring, i.e., the greater the blurring. This is used to characterize the difference in attention performance between the j-th image region and the f-th image region in the l-th high-frequency image. The smaller the difference in attention performance, the smaller the high-frequency information difference, i.e., the smaller the ambiguity.

This embodiment utilizes the differences in grayscale representation and attention representation between the first and second image regions to accurately determine the blurriness between them. This helps in subsequently determining the blur weight of the image region in the edge blur function based on the blurriness, thereby improving the compression effect of remote sensing images.

As an optional embodiment, S205 may specifically include:

For each image region, perform the following steps:

Obtain the planar distance between target image regions in each high-frequency image, where the target image region is any image region;

The blur weight of the target image region is determined by using the blur between the target image region and its neighboring regions, as well as the distance between each plane.

In this embodiment, the fuzzy weights can be determined using the following formula 4:

Formula 4

In formula 4, The blur weights used to characterize the j-th image region The term is used to characterize the blur between the j-th image region and the r-th neighboring region, and n is used to characterize the number of neighboring regions. The planar distance between the centroid of the j-th image region in the l-th high-frequency image and the centroid of the j-th image region in the c-th high-frequency image is used to characterize the number of high-frequency images.

The greater the ambiguity between the j-th image region and its neighboring regions, the greater the ambiguity weight of the j-th image region; the greater the planar distance between the j-th image regions in each high-frequency image, the greater the ambiguity weight of the j-th image region.

This embodiment utilizes the ambiguity between the target image region and its neighboring regions, as well as the planar distance between the target image regions in each high-frequency image, to accurately determine the ambiguity weight of the target image region, thereby improving the compression effect of remote sensing images.

As an optional embodiment, as shown in FIG3, S104 may specifically include the following S301 to S304:

S301, Create a blur function for each image region based on the blur weight of each image region;

S302, the optimal solution of each fuzzy function is obtained by using the gradient descent method;

S303, Determine the edge constraint coefficient of each image region based on the optimal solution of each fuzzy function;

S304. Based on the edge constraint coefficients, the high-resolution remote sensing image is compressed to obtain a compressed remote sensing image.

In this embodiment, the edge definition coefficient is used to measure the degree of blurring of edges in the image region.

As an example, the fuzzy function can be specifically determined using the following formula 5:

Formula 5

In Formula 5, f is used to characterize the target value of the fuzzy function. The blur weights used to characterize the j-th image region The mean value used to characterize the blur weights of each image region.

Then, the server uses gradient descent to obtain the optimal solution for the blur function of each image region based on the obtained blur function. A larger optimal solution indicates a more blurred edge in the corresponding image region within the high-resolution remote sensing image. In this case, edge constraint is more necessary to avoid edge loss during encoding via the Region of Interest (ROI), resulting in higher quality decoded high-resolution remote sensing images. Then, based on the optimal solution of each blur function, edge constraint coefficients are set for each image region.

Finally, the pixels of the high-resolution remote sensing image are quantized according to the edge constraint coefficient. For pixels with a small edge constraint coefficient (i.e., pixels with a high degree of blur), a larger quantization step size can be used for compression; for pixels with a large edge constraint coefficient (i.e., pixels near the edge), a smaller quantization step size is used to preserve edge details.

In this embodiment, a corresponding fuzzy function is created based on the fuzzy weight of each image region, and the optimal solution of the fuzzy function is obtained using the gradient descent method. Then, based on the optimal solution of the fuzzy function, the edge constraint coefficient of the image region is determined. Finally, based on the edge constraint coefficient, the high-resolution remote sensing image is compressed to obtain a compressed remote sensing image. Thus, by performing hierarchical compression of the high-resolution remote sensing image according to the fuzzy weight of each image region, the compression effect of the remote sensing image can be improved.

As an optional embodiment, S303 may specifically include:

To obtain the maximum and minimum values of a fuzzy function;

Subtracting the minimum value from the maximum value of the function yields the range of the fuzzy function.

By utilizing the optimal solution and range of the fuzzy function, the edge constraint coefficient of the image region is determined.

In this embodiment, the edge definition coefficient of the image region can be determined using the following formula 6:

Formula 6

In formula 6, Used to characterize the edge constraint coefficient Used to characterize the optimal solution of a fuzzy function. The maximum value of a fuzzy function is used to characterize the fuzzy function. The minimum value of a function used to characterize a fuzzy function.

in, The range of the function used to characterize the blur function indicates that the larger the range, the greater the difference in the image region. Therefore, a smaller quantization step size is needed during image compression to preserve edge details, i.e., a larger edge limiting coefficient.

This embodiment utilizes the optimal solution and range of the fuzzy function to determine the edge constraint coefficients of image regions. This allows for graded compression of high-resolution remote sensing images based on the edge constraint coefficients of each region, thereby improving the compression effect of remote sensing images.

This invention is based on a high-resolution remote sensing image transmission method applied to mineral resource exploration. Accordingly, it also provides specific embodiments of a high-resolution remote sensing image transmission system applied to mineral resource exploration.

Figure 4 provides a schematic diagram of a high-resolution remote sensing image transmission system for mineral resource exploration. The high-resolution remote sensing image transmission system 400 for mineral resource exploration includes an image acquisition module 410, an image conversion module 420, a weight setting module 430, and an image compression module 440.

Image acquisition module 410 is used to acquire high-resolution remote sensing images to be transmitted;

The image conversion module 420 is used to convert high-resolution remote sensing images from the spatial domain to the frequency domain through discrete wavelet transform, so as to obtain the remote sensing frequency image corresponding to the high-resolution remote sensing image.

The weight setting module 430 is used to 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. The blur weight is used to characterize the weight of the image region in the edge blur function.

The image compression module 440 is used to compress the high-resolution remote sensing image according to the blur weight of each image region to obtain a compressed remote sensing image for transmission.

The high-resolution remote sensing image transmission system for mineral resource exploration provided in this embodiment first transforms the high-resolution remote sensing image from the spatial domain to the frequency domain using discrete wavelet transform, obtaining the corresponding remote sensing frequency image. Then, based on the pixel density of each image region in the high-frequency image of the remote sensing frequency image, the weights of each image region in the edge blurring function are set. Thus, through discrete wavelet transform, the region of interest (ROI) in the high-resolution remote sensing image can be identified. Furthermore, by setting the weights of each image region in the edge blurring function based on the differences in pixel density between different ROI regions, the information of the ROI can be preserved as completely as possible. Finally, the high-resolution remote sensing image is compressed based on the blur weights of the image regions. In this way, by accurately identifying the ROI and preserving its information as completely as possible, the compression effect of the remote sensing image can be improved.

It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims (10)

1. The high-resolution remote sensing image transmission method applied to mineral resource exploration is characterized by comprising the following steps of:

acquiring a high-resolution remote sensing image to be transmitted;

converting the high-resolution remote sensing image from a space domain to a frequency domain through discrete wavelet transformation to obtain a remote sensing frequency image corresponding to the high-resolution remote sensing image;

Setting fuzzy weights of the image areas according to pixel point density of the image areas in the high-frequency image of the remote sensing frequency image, wherein the fuzzy weights are used for representing weights of the image areas in an edge fuzzy function;

and compressing the high-resolution remote sensing image according to the fuzzy weight of each image area to obtain a compressed remote sensing image, so that the compressed remote sensing image is transmitted.

2. The method for transmitting high-resolution remote sensing images for mineral resource exploration according to claim 1, wherein said setting the fuzzy weight of each image area according to the pixel density of each image area in the high-frequency image of the remote sensing frequency image comprises:

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 areas;

determining a region representation value of each image region according to the neighborhood density of each pixel point in each image region, wherein the region representation value is used for representing the quantity of high-frequency information contained in the image region;

Determining the region attention of each image region according to the difference between the region representation value of each image region and the region representation value of the corresponding neighborhood region;

Determining the ambiguity between the image areas according to the difference value of the regional attention degree and the difference value of the gray value between the image areas;

And setting the fuzzy weight of each image area according to the fuzzy degree between the image areas.

3. The method for transmitting high-resolution remote sensing image applied to mineral resource exploration according to claim 2, wherein 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 areas comprises:

Screening out the high-frequency images from the remote sensing frequency images;

In the high-frequency image, calculating the neighborhood density of each pixel point by taking each pixel point as a center;

and clustering the pixel points according to the neighborhood density of the pixel points to obtain a plurality of image areas.

4. The method for transmitting high-resolution remote sensing images for mineral resource exploration according to claim 2, wherein said determining the area representation value of each of said image areas according to the neighborhood density of each of said pixels in each of said image areas comprises:

for each image area, the following steps are respectively executed:

Acquiring the number of target pixel points of a target image area and the maximum number of pixel points in each image area, wherein the target image area is any one image area;

carrying out average value calculation on the neighborhood density of each pixel point in the target image area to obtain the pixel point density of the target image area;

and determining the region representation value of the target image region by using the pixel point density of the target image region, the number of the target pixels and the maximum number of the pixels.

5. The method for transmitting high-resolution remote sensing images for mineral resource exploration according to claim 2, wherein determining the regional attention of each image region according to the difference between the regional representation value of each image region and the regional representation value of the corresponding neighborhood region comprises:

for each image area, the following steps are respectively executed:

Acquiring Euclidean distance between a target image area and each corresponding neighborhood area, wherein the target image area is any one image area;

Taking absolute values after making differences between the region representation values of the target image region and the region representation values of the neighborhood regions to obtain a plurality of region representation differences;

and determining the regional attention of the target image region by using the Euclidean distance and the regional performance difference.

6. The method for transmitting high-resolution remote sensing images for mineral resource exploration according to claim 2, wherein said determining the ambiguity between the image areas according to the difference of the regional interest degree and the difference of the gray values between the image areas comprises:

Acquiring a gray average value of a first image area and a gray average value of a second image area, wherein the first image area and the second image area are any two different image areas in the image areas;

taking an absolute value after the gray average value of the first image area is differenced from the gray average value of the second image area, and obtaining gray expression difference;

Taking an absolute value after the regional attention of the first image region is differed from the regional attention of the second image region, and obtaining attention expression difference;

and determining the ambiguity between the first image region and the second image region by using the gray level representation difference and the attention level representation difference.

7. The method for transmitting high-resolution remote sensing images for mineral resource exploration according to claim 2, wherein said setting the blur weight of each of said image areas according to the blur degree between each of said image areas comprises:

for each image area, the following steps are respectively executed:

Obtaining a plane distance between target image areas in each high-frequency image, wherein each target image area is any one image area;

And determining the fuzzy weight of the target image area by using the fuzzy degree between the target image area and each neighborhood area and each plane distance.

8. The method for transmitting high-resolution remote sensing images applied to mineral resource exploration according to claim 1, wherein the compressing the high-resolution remote sensing images according to the fuzzy weight of each image area to obtain compressed remote sensing images comprises:

Creating a fuzzy function of each image area according to the fuzzy weight of each image area;

Obtaining the optimal solution of each fuzzy function through a gradient descent method;

determining edge limiting coefficients of the image areas according to the optimal solutions of the fuzzy functions;

And compressing the high-resolution remote sensing image according to each edge limiting coefficient to obtain the compressed remote sensing image.

9. The method of claim 8, wherein determining the edge defining coefficients of each image region according to the optimal solution of each blur function comprises:

Obtaining a function maximum value and a function minimum value of the fuzzy function;

Subtracting the function minimum value from the function maximum value to obtain a function value domain of the fuzzy function;

and determining the edge limiting coefficient of the image area by utilizing the optimal solution of the fuzzy function and the function value range of the fuzzy function.

10. A high resolution remote sensing image transmission system for mineral resource exploration, the system comprising:

the image acquisition module is used for acquiring a high-resolution remote sensing image to be transmitted;

The image conversion module is used for converting the high-resolution remote sensing image from a space domain to a frequency domain through discrete wavelet transformation to obtain a remote sensing frequency image corresponding to the high-resolution remote sensing image;

the weight setting module is used for setting fuzzy weights of the image areas according to pixel point density of the image areas in the high-frequency image of the remote sensing frequency image, and the fuzzy weights are used for representing weights of the image areas in an edge fuzzy function;

And the image compression module is used for compressing the high-resolution remote sensing image according to the fuzzy weight of each image area to obtain a compressed remote sensing image so as to transmit the compressed remote sensing image.