Method, medium and system for generating layer by cutting on-line satellite image

Abstract

The invention provides a method, a medium and a system for generating a layer by cutting an online satellite image, belongs to the technical field of image data processing, and particularly relates to partition storage and hierarchical management of to-be-processed satellite image data by establishing a satellite image spatial index database, a multi-level image pyramid and a multi-level image memory pool. And obtaining to-be-processed image slice data from the target pyramid hierarchy based on parameters in the image layer cutting request, distributing the to-be-processed image slice data to the corresponding target memory pool hierarchy, performing atmospheric correction processing by adopting a radiation transfer equation set and performing edge feathering processing by adopting a weight calculation equation set, and finally generating target image layer data. Wherein the multi-level image pyramid is stored in a system cache, meanwhile, a multi-level image memory pool is established, and the level number of the multi-level image memory pool corresponds to the level number of the multi-level image pyramid in a one-to-one mode. The technical problem that in the prior art, the quality of a cut layer formed after splicing is not ideal is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of image data processing, and in particular, relates to a method, medium and system for online satellite image cropping and layer generation. Background Art

[0002] With the rapid development of remote sensing technology and satellite image applications, satellite image data is increasingly used in geographic information systems, natural resource surveys, urban planning, environmental monitoring and other fields. Satellite image data has the characteristics of wide spatial range, high resolution, and high frequency. The amount of single-view image data usually reaches several GB or even tens of GB. In practical applications, users often need to process massive satellite image data online in real time to obtain image layers of specific areas.

[0003] At present, the mainstream satellite image processing system adopts the traditional batch processing mode, that is, the whole scene image data is downloaded to the local computer first, and then processed by cropping, correction, etc. This processing mode has problems such as large data transmission volume, long processing time, and high system resource consumption. Especially in the application scenario of network services, users need to quickly obtain image data of the area of ​​interest, and the traditional batch processing mode is difficult to meet the real-time requirements.

[0004] Existing online satellite image processing technologies mainly use preprocessing and caching strategies, that is, pre-processing and atmospheric correction of satellite images, and storing the processing results in the cache. Although this method can improve data access speed, the preset block size and correction parameters in the preprocessing method may not match the actual needs of users, resulting in unsatisfactory quality of the cropped layer formed after stitching. Summary of the invention

[0005] In view of this, the present invention provides a method, medium and system for online satellite image cropping and layer generation, which can solve the technical problem of unsatisfactory quality of cropped layers formed after splicing in the prior art.

[0006] The present invention is achieved in that: The first aspect of the present invention provides a method for online satellite image clipping and layer generation, comprising: by establishing a satellite image spatial index database, a multi-level image pyramid and a multi-level image memory pool, partitioning storage and hierarchical management of satellite image data to be processed, obtaining the image slice data to be processed from the target pyramid level based on the parameters in the layer clipping request and allocating it to the corresponding target memory pool level, using a radiation transfer equation group to perform atmospheric correction processing and a weight calculation equation group to perform edge feathering processing, and finally generating target layer data.

[0007] On the basis of the above technical solution, the method for online satellite image cutting and generating layers of the present invention can also be improved as follows: Among them, the establishment of the satellite image spatial index database is specifically: partitioning and storing the satellite image data to be processed, and the satellite image data to be processed includes the original data of the solar zenith angle, the original data of the atmospheric irradiance, the original data of the atmospheric optical thickness, the original data of the scattering phase function, and the original data of the surface reflectivity.

[0008] Furthermore, the establishment of a multi-level image pyramid and a multi-level image memory pool is specifically as follows: a multi-level image pyramid is generated for the satellite image data to be processed according to a preset image zoom level, and the multi-level image pyramid is stored in a system cache, and a multi-level image memory pool is established at the same time, wherein the number of levels of the multi-level image memory pool corresponds one-to-one to the number of levels of the multi-level image pyramid.

[0009] Furthermore, the parameters in the layer clipping request are specifically: receiving a layer clipping request, and locating the target satellite image data from the satellite image spatial index database according to the spatial range parameters and target resolution parameters included in the layer clipping request.

[0010] Furthermore, the obtaining of the image slice data to be processed from the target pyramid level is specifically as follows: selecting a corresponding target pyramid level in the multi-level image pyramid according to the target resolution parameter, obtaining the image slice data to be processed in the target pyramid level, and allocating the image slice data to be processed to a target memory pool level in the multi-level image memory pool corresponding to the target pyramid level.

[0011] Furthermore, the radiation transfer equation group includes solar radiation calculation equations, atmospheric scattering calculation equations, surface reflection calculation equations, sensor reception calculation equations, and radiation component decomposition calculation matrices, which are used to perform atmospheric correction processing on the image slice data to be processed in the target memory pool level to obtain corrected image slice data.

[0012] Furthermore, the weight calculation equation group includes a distance weight calculation equation, a gradient weight calculation equation, a brightness weight calculation equation, a comprehensive weight calculation equation, and a weight component decomposition calculation matrix, which are used to dynamically splice the corrected image slice data and perform edge feathering processing in the target memory pool level.

[0013] Furthermore, the generation of target layer data is specifically as follows: performing projection transformation processing on the stitched image data according to the final pixel mixing weight value output by the weight calculation equation group and the apparent radiation brightness value output by the radiation transfer equation group, performing cropping processing on the image data after the projection transformation according to the spatial range parameter, and outputting the cropped image data as target layer data.

[0014] For the radiative transfer equations: The solar radiation calculation equation is specifically expressed as follows: ; In the formula, is the incident radiation flux value, in units of ; is the original data of atmospheric external irradiance, in units of ; is the original data of the solar zenith angle, in radians; is the atmospheric optical thickness parameter; is the atmospheric scattering error term, ranging from 0.01 to 0.05.

[0015] The atmospheric scattering calculation equation is specifically expressed as follows: ; In the formula, is the atmospheric path radiation value, in units of ; is the original data of scattering phase function; is the single scattering albedo, ranging from 0 to 1; It is the original data of atmospheric optical thickness; is the error term for scattering calculation, ranging from 0.02 to 0.06.

[0016] The surface reflection calculation equation is specifically expressed as follows: ; In the formula, is the surface reflected radiation value, in units of ; is the original data of surface reflectivity; is the error term for surface reflection calculation, ranging from 0.01 to 0.04.

[0017] The sensor receiving calculation equation is specifically expressed as follows: ; In the formula, is the apparent radiance value, in units of ; is the sensor response coefficient, ranging from 0.8 to 1.2; is the sensor channel radiation correction value; is the number of sensor channels; is the sensor receiving error term, ranging from 0.03 to 0.07.

[0018] The radiation component decomposition calculation matrix is ​​specifically expressed as follows: ; In the formula, is the radiation stability component matrix value; is the radiation variation component matrix value; is the decomposition coefficient matrix element, which is obtained by the least squares method.

[0019] The construction principle and meaning of each equation in the radiation transfer equations are as follows: 1. The solar radiation calculation equation is based on the Beer-Lambert law and takes into account the atmospheric attenuation effect. The exponential term represents the attenuation effect of the atmosphere on solar radiation, and the cosine term represents the influence of the sun's incident angle; 2. The atmospheric scattering calculation equation is based on the radiation transfer theory and takes into account the effects of single scattering and multiple scattering. The term represents the solid angle integral of the scattering, and the exponential term represents the optical path attenuation; 3. The surface reflection calculation equation is based on the Lambertian reflection model, which takes into account the isotropic reflection characteristics of the surface. The term is used for normalization, and the exponential term represents the atmospheric transmittance; 4. The sensor receiving calculation equation adopts the linear superposition principle and takes into account the contribution of various radiation components. The summation term represents the response characteristics of different bands; 5. The radiation component decomposition matrix uses a linear decomposition method to decompose the total radiation into stable components and variable components, which is helpful for subsequent radiation correction.

[0020] For the weight calculation system: The distance weight calculation equation is specifically expressed as follows: ; In the formula, is the initial distance weight value, ranging from 0 to 1; is the pixel coordinate value of the overlapping area; is the coordinate value of the seam line in the overlapping area; is the distance impact factor, ranging from 1 to 5; The error term for distance weight calculation ranges from 0.01 to 0.03.

[0021] The gradient weight calculation equation is specifically expressed as follows: ; In the formula, is the initial gradient weight value, ranging from 0 to 1; is the local pixel value of the overlapping area; is the local window radius, ranging from 2 to 5; is the pixel gradient modulus; is the gradient impact factor, ranging from 10 to 50; The error term is calculated for the gradient weight, ranging from 0.02 to 0.04.

[0022] The brightness weight calculation equation is specifically expressed as follows: ; In the formula, is the initial brightness weight value, ranging from 0 to 1; is the current pixel value; is the average brightness value of the local area; is the brightness impact factor, ranging from 15 to 45; is the pixel value of the corresponding position of the adjacent image; Calculate the error term for brightness weight, ranging from 0.01 to 0.05.

[0023] The comprehensive weight calculation equation is specifically expressed as follows: ; In the formula, is the initial pixel mixing weight value, ranging from 0 to 1; is the weight combination coefficient, and satisfies ; is the weight index term, ranging from 0.5 to 2; is the error term for comprehensive weight calculation, ranging from 0.02 to 0.06.

[0024] The weight component decomposition calculation matrix is ​​specifically expressed as follows: ; In the formula, is the weight stable component matrix value; is the weight change component matrix value; is the weight decomposition coefficient matrix element, obtained by singular value decomposition.

[0025] The final pixel mixing weight value calculation equation is specifically expressed as follows: ; In the formula, is the final pixel blending weight value, ranging from 0 to 1; is the combined coefficient of the stable component and the variable component, and satisfies ; is the attenuation factor, ranging from 3 to 8; The error term is calculated for the final weight, ranging from 0.01 to 0.04.

[0026] The construction principle and meaning of each equation in the weight calculation equation group are as follows: 1. The distance weight calculation equation uses a Gaussian function, which reflects the characteristic that the weight decays with increasing distance, which helps to maintain the smoothness of edge transition; 2. The gradient weight calculation equation combines local difference and global gradient information. The fractional part is used to suppress local noise, and the exponential part is used to maintain edge features. 3. The brightness weight calculation equation takes into account the difference between the pixel and the local mean as well as the brightness consistency of adjacent images. The fractional part is used to deal with the brightness jump at the seam; 4. The comprehensive weight calculation equation adopts the power function combination form, and adjusts the contribution of each component through the exponential term, which improves the flexibility and adaptability of weight calculation; 5. The weight component decomposition matrix decomposes the weight into stable and variable parts through linear transformation, which is convenient for separate control and optimization; 6. The final weight calculation equation adopts a weighted combination with attenuation, and the exponential attenuation term is used to suppress high-frequency changes, thereby improving the stability of the splicing results.

[0027] The establishment and management of the image memory pool adopts a multi-level image memory pool establishment and management equation group, which is specifically expressed as follows: The equation for calculating the number of levels is: ; In the formula, The number of levels of the multi-level image memory pool; is the original image resolution; is the target minimum resolution; It is a hierarchical redundancy item, with a value of 1~2; Indicates a round-up operation.

[0028] The calculation equation for the memory capacity of each layer is: ; In the formula, For the Layer memory pool capacity, in MB; is the memory capacity of the base layer, in MB; is the capacity attenuation coefficient, ranging from 1.5 to 2.5; For the Layer memory redundancy, ranging from 10 to 50 MB.

[0029] The memory allocation optimization equation: ; In the formula, For the The actual amount of memory allocated to the layer, in MB; For the Layer access frequency factor, ranging from 0 to 1; For the Layer data density factor, ranging from 0 to 1; The total available memory of the system in MB; This is the memory allocation error term, ranging from 5 to 15 MB.

[0030] The memory load balancing equation: ; In the formula, For the Layer load index; For the The amount of memory currently used by the layer, in MB; For the Layer data access time, in ms; is the maximum allowed access time, in ms; For the Layer cache queue length; is the maximum allowed queue length; is the balance coefficient and satisfies ; is the load calculation error term, ranging from 0.01 to 0.05.

[0031] The memory dynamic adjustment equation: ; In the formula, For the Layer memory adjustment amount, in MB; is the system average load index; is the adjustment coefficient, ranging from 0.1 to 0.5; is the load change rate; is the adjustment error term, ranging from 1 to 5 MB.

[0032] The establishment and construction principles and significance of the multi-level image memory pool and the equations in the management equation group are as follows: 1. The equation for calculating the number of levels is based on the resolution ratio, and uses a logarithmic form to ensure the rationality of the level division, and the redundancy term ensures the fault tolerance of the system; 2. The memory capacity calculation equation uses an exponential decay model, which reflects the decreasing characteristics of high-level data volume. Memory redundancy is used to handle data fluctuations. 3. The memory allocation optimization equation takes into account two key factors: access frequency and data density, and uses normalization to ensure the rationality of allocation; 4. The load balancing equation comprehensively considers the three dimensions of memory usage, access time and queue length, and achieves multi-objective optimization through weighted method; 5. The dynamic adjustment equation takes into account both load differences and change trends, and uses proportional regulation to achieve smooth adjustment and avoid drastic fluctuations.

[0033] The one-to-one correspondence between the number of levels of the multi-level image memory pool and the number of levels of the multi-level image pyramid is specifically expressed as follows: ; In the formula, The number of levels of the multi-level image memory pool; is the number of levels of the multi-level image pyramid; is the total number of levels.

[0034] The mapping relationship equation between the levels: ; In the formula, For the Layer memory pool space; For the Layer pyramid image space; Indicates a one-to-one correspondence.

[0035] The corresponding level data consistency constraint equation: ; In the formula, For the Layer memory pool resolution; For the Layer pyramid resolution; is the original resolution; is the resolution error term, ranging from 0.1 to 0.5 pixels.

[0036] The construction principles and significance of these corresponding relationship equations are as follows: 1. The one-to-one correspondence between the number of levels ensures the strict structural correspondence between the memory pool and the image pyramid, which facilitates data management and access; 2. The mapping relationship equation between the levels establishes the spatial mapping rules, ensuring the accurate correspondence of data between the memory pool and the pyramid; 3. The corresponding hierarchical data consistency constraint equation stipulates the decreasing relationship of resolution, ensuring the continuity and consistency of data between different levels.

[0037] For step S70: Projection transformation matrix: ; In the formula, is the scale transformation parameter, ranging from 0.5 to 2.0; is the shear transformation parameter, ranging from -0.5 to 0.5; is the translation transformation parameter, in pixels; is the perspective transformation parameter, ranging from -0.01 to 0.01; is a normalized parameter, usually 1; the projection transformation matrix parameters are calculated by the following equation: ; ; ; ; In the formula, is the scaling factor of the x-axis and y-axis; is the rotation angle in radians; Perform projection transformation on the color-corrected image data: ; In the formula, is the coordinate after projection, in pixels; is the original coordinate, in pixels; is the scale factor used for normalization and is calculated as: ; is the projection error term, ranging from 0.1 to 0.3 pixels; the final projection coordinates are calculated as: ; Coordinate registration of the image data after projection transformation: ; In the formula, is the projection point coordinate, in pixels; is the coordinate of the corresponding point, in pixels; is the regularization coefficient, ranging from 0.01 to 0.1; To optimize the error term, the range is 0.1~0.3 pixels; is the number of control points, not less than 4; the projection correction optimization process adopts an iterative method: ; In the formula, is the number of iterations; is the learning rate, ranging from 0.001 to 0.01; Evaluate the projection quality: ; In the formula, Score the projection quality in pixels; is the coordinate of the control point after projection; is the coordinate of the target control point; To evaluate the error term, the range is 0.05~0.15 pixels; For step S80: Determine the clipping boundary based on the spatial extent parameters: ; In the formula, is the boundary indicator function; The clipping area consists of four vertex coordinates Sure; is the boundary error term, ranging from 0.5 to 1.5 pixels; the following constraints are used to determine the cropping area: ; ; In the formula, The coordinates of the upper left corner of the cropping area; The coordinates of the lower right corner of the cropping area; Smooth the cropped edge area: ; In the formula, is the smoothed boundary; is the Gaussian kernel standard deviation, ranging from 1.0 to 3.0; are the coordinates of the center of the Gaussian kernel; Represents a two-dimensional convolution operation; is the smoothing error term, ranging from 0.2 to 0.6 pixels; To perform boundary optimization: ; In the formula, To optimize the boundary; is the edge strength of the original image; is the boundary gradient; is the balance coefficient, ranging from 0.1 to 0.5; To optimize the error term, the range is 0.3~0.8 pixels; the boundary gradient calculation is: ; For step S90: The target layer data is compressed and encoded: ; In the formula, is the compressed image; is the original image; is the transform domain coefficient; is the compression coefficient, ranging from 0.1 to 1.0; is the compression error term, ranging from 0.01 to 0.05; the bit rate calculation of the compression process is: ; In the formula, is the bit rate; For the The probability of the level of quantization; is the quantization level; Evaluate compression quality: ; In the formula, is the maximum value of pixels; is the mean square error; is the image block mean; is the standard deviation; is the covariance; are constant terms, respectively ; is the pixel dynamic range; is the quality assessment weight coefficient and satisfies ; To evaluate the error term, the range is 0.01~0.05; Optimize compression encoding: ; In the formula, is the pixel probability distribution; is the coding coefficient; Pixel The neighborhood set of ; is the sparsity parameter, ranging from 0.01 to 0.1; is the spatial consistency parameter, ranging from 0.05 to 0.15; is the coding error term, ranging from 0.02 to 0.06; the coding optimization adopts an iterative method: ; In the formula, is the number of iterations; is the update step size, ranging from 0.001 to 0.01.

[0038] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for online satellite image cropping and generating layers.

[0039] A third aspect of the present invention provides a system for online satellite image cropping and layer generation, which includes the above-mentioned computer-readable storage medium.

[0040] Compared with the prior art, the method, medium and system for online satellite image cropping and layer generation provided by the present invention first establish an efficient satellite image spatial index database, and partition management and rapid retrieval of massive image data through multi-level spatial grids. At the same time, the multi-level image pyramid and dynamic memory pool are adopted to achieve rapid access and efficient caching of original image data.

[0041] In terms of image preprocessing, the present invention uses a complete set of radiation transfer models and weight calculation algorithms to completely eliminate radiation distortion caused by factors such as atmospheric effects, surface reflections, and sensor characteristics, and effectively solves the brightness inconsistency and edge discontinuity problems in the image stitching process through an adaptive pixel stitching strategy. In addition, a geometric correction method based on projection transformation and optimized registration is used to ensure the geometric accuracy of the output image.

[0042] Finally, the present invention realizes the processing of image data cutting, resolution resampling, format conversion, compression encoding, etc. to meet the needs of GIS applications, and generates target layer data that meets user needs. Compared with the existing batch processing method, this method has higher efficiency and flexibility, and solves the technical problem of unsatisfactory quality of the cut layer formed after splicing in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of the method provided by the present invention; Figure 2 This is a schematic diagram of control point distribution in Example 2. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0045] like Figure 1 FIG. 1 is a flow chart of a method for online satellite image cropping and layer generation provided by the first aspect of the present invention. The method comprises the following steps: S10, establishing a satellite image spatial index database, partitioning and storing the satellite image data to be processed, wherein the satellite image data to be processed includes original data of solar zenith angle, original data of atmospheric external irradiance, original data of atmospheric optical thickness, original data of scattering phase function, and original data of surface reflectivity; S20, generating a multi-level image pyramid for the to-be-processed satellite image data according to a preset image zoom level, storing the multi-level image pyramid in a system cache, and establishing a multi-level image memory pool, wherein the number of levels in the multi-level image memory pool corresponds one-to-one to the number of levels in the multi-level image pyramid; S30, receiving a layer clipping request, and locating target satellite image data from the satellite image spatial index database according to a spatial range parameter and a target resolution parameter included in the layer clipping request; S40, selecting a corresponding target pyramid level in the multi-level image pyramid according to the target resolution parameter, acquiring the to-be-processed image slice data in the target pyramid level, and allocating the to-be-processed image slice data to a target memory pool level in the multi-level image memory pool corresponding to the target pyramid level; S50, performing atmospheric correction processing on the image slice data to be processed by using a group of radiation transfer equations in the target memory pool level to obtain corrected image slice data; S60, dynamically splicing the corrected image slice data in the target memory pool level, and performing edge feathering processing using a weight calculation equation group; S70, performing projection transformation processing on the spliced ​​image data according to the final pixel mixing weight value output by the weight calculation equation group and the apparent radiation brightness value output by the radiation transfer equation group; S80, performing cropping processing on the image data after the projection transformation according to the spatial range parameter; S90: Output the cropped image data as target layer data.

[0046] The specific implementation methods of the above steps are described in detail below: The specific implementation method of step S10 is: first, establish a spatial partition index table in the satellite image spatial index database. Specifically, this step includes: step 101, establish a multi-level spatial grid according to the geographical location coordinates, and establish a spatial partition index table in the satellite image spatial index database. In this way, the satellite image data to be processed can be better spatially managed and retrieved. Next, in step 102, the original data of the solar zenith angle, the original data of the atmospheric irradiance, the original data of the atmospheric optical thickness, the original data of the scattering phase function, and the original data of the surface reflectivity in the satellite image data to be processed are collected. These original data are the basis for subsequent image processing. Afterwards, in step 103, the satellite image data to be processed is spatially partitioned and stored, and a spatial partition table is established. In this way, the efficiency of data retrieval can be improved. Finally, in step 104, the satellite image spatial index database is updated according to the multi-level spatial grid and the spatial partition table. This step aims to establish an efficient satellite image spatial index system.

[0047] The specific implementation method of step S20 is: first, in step 201, the number of levels of the multi-level image pyramid is determined, and the multi-level image memory pool is established. This is to prepare for subsequent multi-scale processing. Next, in step 202, the resolution of each level is calculated according to the level number calculation equation. This ensures that the resolution relationship between different levels is reasonable. Then in step 203, memory space is allocated to each level of the multi-level image memory pool based on the memory capacity calculation equation. This can better manage memory resources. Next, in step 204, the multi-level image memory pool is optimized and configured according to the memory allocation optimization equation. This step aims to improve memory utilization efficiency. Then in step 205, the multi-level image memory pool is initialized using the load balancing equation. This ensures that the load of the memory pool is balanced in the initial state. Finally, in step 206, a dynamic management mechanism of the multi-level image memory pool is established based on the memory dynamic adjustment equation. This can cope with changes in data access patterns.

[0048] The specific implementation method of step S30 is: first, in step 301, a layer clipping request is received, and the spatial range parameters in the layer clipping request are parsed. This is to determine the target area. Next, in step 302, the target area is retrieved from the satellite image spatial index database according to the spatial range parameters. This helps to quickly locate the target image data. Then in step 303, the target resolution parameter in the layer clipping request is parsed. This provides a basis for subsequent image scaling. Then in step 304, the layer scaling ratio is calculated according to the target resolution parameter. In this way, the resolution of the final output layer can be determined. Finally, in step 305, the target satellite image data is located from the satellite image spatial index database according to the target area and the layer scaling ratio. This step realizes the rapid positioning of the target image data.

[0049] The specific implementation of step S40 is: first, in step 401, the target pyramid level is calculated according to the target resolution parameter. This provides a basis for subsequent data access. Next, in step 402, the target pyramid level is located in the multi-level image pyramid. In this way, the required image data can be quickly accessed. Then in step 403, the image slice data to be processed is read from the target pyramid level. This prepares data for subsequent image processing. Then in step 404, the target memory pool level is located in the multi-level image memory pool. This ensures that the data is allocated to the appropriate memory level. Next, in step 405, the image slice data to be processed is allocated to the target memory pool level. This can improve data access efficiency. Finally, in step 406, the load status of the multi-level image memory pool is updated according to the load balancing equation. This helps to dynamically adjust the usage of the memory pool.

[0050] The specific implementation method of step S50 is: using the radiation transfer equation group to calculate the radiation correction coefficient matrix value, and performing matrix operation on the radiation correction coefficient matrix value and the image slice data to be processed, correcting the solar radiation flux, atmospheric scattering, surface reflection and sensor response in the image slice data to be processed, and correcting the radiation value of the image slice data to be processed according to the correction result to obtain the corrected image slice data. This step aims to eliminate the radiation distortion caused by factors such as atmospheric effects and surface reflection in the image. At the same time, the weight calculation equation group is used to calculate the final pixel mixing weight value to prepare for the subsequent splicing process. Specifically, this step uses the solar radiation calculation equation, the atmospheric scattering calculation equation, the surface reflection calculation equation, the sensor reception calculation equation and the radiation component decomposition calculation matrix, etc., and comprehensively considers the influence of atmospheric and surface factors on the image radiation value, thereby realizing effective radiation correction. At the same time, the application of the weight calculation equation group provides a reliable weight basis for the subsequent image splicing process.

[0051] The specific implementation of step S60 is: using the weight calculation equation group to calculate the final pixel mixing weight value. Specifically, this step applies the distance weight calculation equation, the gradient weight calculation equation, the brightness weight calculation equation, the comprehensive weight calculation equation, and the weight component decomposition calculation matrix. Among them, the distance weight calculation equation is based on the Gaussian function, which reflects the characteristic that the weight decays with increasing distance, and helps to maintain the smoothness of edge transition. The gradient weight calculation equation combines local difference and global gradient information, which can suppress local noise and maintain edge features. The brightness weight calculation equation takes into account the difference between the pixel and the local mean and the brightness consistency of adjacent images at the same time, which helps to deal with the brightness jump at the seam. The comprehensive weight calculation equation adopts the power function combination form, and the contribution of each component can be flexibly controlled by adjusting the exponential term. The weight component decomposition matrix decomposes the weight into two parts, stable and variable, through linear transformation, which is convenient for optimization. The final weight calculation equation adopts the weighted combination form with attenuation, which can effectively suppress high-frequency changes and improve the stability of the splicing result. In general, this step makes full use of various weight calculation methods to provide a high-quality pixel mixing basis for subsequent splicing processing.

[0052] The specific implementation of step S70 is: first, in step 701, the final pixel mixing weight value and the apparent radiance value are obtained. These data will provide a basis for subsequent processing. Next, in step 702, the spliced ​​image data is subjected to brightness balance processing according to the final pixel mixing weight value. This helps to eliminate brightness inconsistencies in the image. Then, in step 703, the image data subjected to brightness balance processing is subjected to color correction according to the apparent radiance value. This ensures that the color representation of the image is more accurate. Then, in step 704, a projection transformation matrix is ​​established. The matrix includes parameters such as scale transformation, shear transformation, translation transformation, and perspective transformation, which can achieve geometric correction of the image. Next, in step 705, the image data subjected to color correction is subjected to projection transformation processing. This step can eliminate geometric distortion in the image. Finally, in step 706, the image data after projection transformation is subjected to coordinate registration. This ensures that the geographical location information in the image is consistent with the actual situation. In general, this step realizes the processing of brightness balance, color correction, geometric correction, and coordinate registration of the image, and prepares for subsequent cutting and output.

[0053] The specific implementation method of step S80 is: first, in step 801, the cutting boundary is determined according to the spatial range parameter. This provides a basis for the subsequent cutting process. Next, in step 802, the image data after the projection transformation is subjected to boundary checking. This helps to identify abnormal areas at the edge of the image. Then, in step 803, the image data after the projection transformation is subjected to cutting according to the cutting boundary. This can obtain the desired target area image. Then, in step 804, the edge area after cutting is smoothed. This can eliminate the jagged effect caused by cutting. Next, in step 805, the image data after cutting is resampled and the boundary is optimized according to the target resolution parameter to obtain the image data after cutting. This step can ensure that the output image meets the required resolution requirements. In general, this step realizes the processing of image cutting, edge smoothing, and resolution resampling, and prepares for the final output.

[0054] The specific implementation method of step S90 is: first, in step 901, the format of the image data after the cropping process is converted. This can ensure that the image data meets the general output format requirements. Next, in step 902, the target layer metadata is generated. These metadata contain key attributes such as geographic information, resolution, and band of the image. Then in step 903, the target layer metadata is associated with the image data after the cropping process. This can ensure the consistency between the metadata and the image data. Then in step 904, the associated data is organized into target layer data. This step realizes the packaging output of image data and metadata. Next, in step 905, the target layer data is compressed and encoded. This can reduce the volume of output data and improve transmission and storage efficiency. Finally, in step 906, the compressed and encoded target layer data is output and saved. In this way, the entire image processing process is completed and the required target layer data is obtained. In general, this step realizes the processing of format conversion, metadata generation, data packaging, and compression encoding of image data, ensuring the quality and efficiency of the final output.

[0055] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for online satellite image cropping and generating layers.

[0056] A third aspect of the present invention provides a system for online satellite image cropping and layer generation, which includes the above-mentioned computer-readable storage medium.

[0057] Specifically, the principle of the present invention is to make full use of the temporal and spatial characteristics of satellite image data, adopt a multi-level data organization and processing mechanism, and achieve efficient management and rapid response to massive raw image data.

[0058] First, the satellite image spatial index database is established to partition and store image data through multi-level spatial grids, which greatly improves the efficiency of data retrieval. At the same time, the dynamic maintenance of the spatial partition table can quickly locate and obtain the image data of the target area. This lays a good foundation for subsequent image processing.

[0059] Secondly, the multi-level image pyramid and dynamic memory pool are used to achieve fast access and efficient caching of original image data. By reasonably dividing the pyramid levels, different resolution requirements are met and data redundancy is reduced. The dynamic memory management mechanism ensures that frequently accessed data can be kept in memory, improving processing efficiency. This hierarchical data organization and adaptive caching mechanism are the key to the high-performance implementation of the present invention.

[0060] In terms of image preprocessing, the present invention adopts a comprehensive correction model based on radiation transfer theory, including solar radiation calculation, atmospheric scattering calculation, surface reflection calculation and sensor response calculation, etc., which effectively eliminates various radiation distortions. At the same time, in order to solve the problems of inconsistent brightness and discontinuous edges in the splicing process, an adaptive pixel mixing weighted algorithm is proposed, which makes full use of various information such as distance, gradient and brightness to generate smooth and natural splicing results. In addition, geometric correction adopts optimized projection transformation and control point registration method, which can accurately eliminate the geometric distortion of the image. The high integration of these preprocessing technologies ensures the radiation and geometric accuracy of the final output image.

[0061] Finally, in response to the needs of GIS applications, the present invention realizes the processing of image data cropping, resolution resampling, format conversion, compression coding, etc. Among them, the cropping and edge smoothing algorithms ensure the clear boundaries of the output image; the resolution resampling and optimized coding technology ensure that the output image still maintains a high visual quality under limited data volume. In general, this method fully meets the user's demand for customized, high-quality satellite image data, and provides strong support for various spatial information services.

[0062] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows: The specific implementation of step S10 is: first, establish a spatial partition index table in the satellite image spatial index database. Specifically, this step includes: step 101, establish a multi-level spatial grid according to the geographical location coordinates, and establish a spatial partition index table in the satellite image spatial index database. In this way, the satellite image data to be processed can be better spatially managed and retrieved. Next, in step 102, the original data of the solar zenith angle, the original data of the atmospheric external irradiance, the original data of the atmospheric optical thickness, the original data of the scattering phase function, and the original data of the surface reflectivity in the satellite image data to be processed are collected. These original data are the basis for subsequent image processing. Afterwards, in step 103, the satellite image data to be processed is spatially partitioned and stored, and a spatial partition table is established. In this way, the efficiency of data retrieval can be improved. Finally, in step 104, the satellite image spatial index database is updated according to the multi-level spatial grid and the spatial partition table. This step aims to establish an efficient satellite image spatial index system.

[0063] The specific implementation method of step S20 is: first, in step 201, the number of levels of the multi-level image pyramid is determined, and the multi-level image memory pool is established. This is to prepare for subsequent multi-scale processing. Next, in step 202, the resolution of each level is calculated according to the level number calculation equation. This ensures that the resolution relationship between different levels is reasonable. Then in step 203, memory space is allocated to each level of the multi-level image memory pool based on the memory capacity calculation equation. This can better manage memory resources. Next, in step 204, the multi-level image memory pool is optimized and configured according to the memory allocation optimization equation. This step aims to improve memory utilization efficiency. Then in step 205, the multi-level image memory pool is initialized using the load balancing equation. This ensures that the load of the memory pool is balanced in the initial state. Finally, in step 206, a dynamic management mechanism of the multi-level image memory pool is established based on the memory dynamic adjustment equation. This can cope with changes in data access patterns.

[0064] The specific implementation method of step S30 is: first, in step 301, a layer clipping request is received, and the spatial range parameters in the layer clipping request are parsed. This is to determine the target area. Next, in step 302, the target area is retrieved from the satellite image spatial index database according to the spatial range parameters. This helps to quickly locate the target image data. Then in step 303, the target resolution parameter in the layer clipping request is parsed. This provides a basis for subsequent image scaling. Then in step 304, the layer scaling ratio is calculated according to the target resolution parameter. In this way, the resolution of the final output layer can be determined. Finally, in step 305, the target satellite image data is located from the satellite image spatial index database according to the target area and the layer scaling ratio. This step realizes the rapid positioning of the target image data.

[0065] The specific implementation of step S40 is: first, in step 401, the target pyramid level is calculated according to the target resolution parameter. This provides a basis for subsequent data access. Next, in step 402, the target pyramid level is located in the multi-level image pyramid. In this way, the required image data can be quickly accessed. Then in step 403, the image slice data to be processed is read from the target pyramid level. This prepares data for subsequent image processing. Then in step 404, the target memory pool level is located in the multi-level image memory pool. This ensures that the data is allocated to the appropriate memory level. Next, in step 405, the image slice data to be processed is allocated to the target memory pool level. This can improve data access efficiency. Finally, in step 406, the load status of the multi-level image memory pool is updated according to the load balancing equation. This helps to dynamically adjust the usage of the memory pool.

[0066] The specific implementation method of step S50 is: using the radiation transfer equation group to calculate the radiation correction coefficient matrix value, and performing matrix operation on the radiation correction coefficient matrix value and the image slice data to be processed, correcting the solar radiation flux, atmospheric scattering, surface reflection and sensor response in the image slice data to be processed, and correcting the radiation value of the image slice data to be processed according to the correction result to obtain the corrected image slice data. This step aims to eliminate the radiation distortion caused by factors such as atmospheric effects and surface reflection in the image. At the same time, the weight calculation equation group is used to calculate the final pixel mixing weight value to prepare for the subsequent splicing process. Specifically, this step uses the solar radiation calculation equation, the atmospheric scattering calculation equation, the surface reflection calculation equation, the sensor reception calculation equation and the radiation component decomposition calculation matrix, etc., and comprehensively considers the influence of atmospheric and surface factors on the image radiation value, thereby realizing effective radiation correction. At the same time, the application of the weight calculation equation group provides a reliable weight basis for the subsequent image splicing process.

[0067] The specific implementation of step S60 is: using the weight calculation equation group to calculate the final pixel mixing weight value. Specifically, this step applies the distance weight calculation equation, the gradient weight calculation equation, the brightness weight calculation equation, the comprehensive weight calculation equation, and the weight component decomposition calculation matrix. Among them, the distance weight calculation equation is based on the Gaussian function, which reflects the characteristic that the weight decays with increasing distance, and helps to maintain the smoothness of edge transition. The gradient weight calculation equation combines local difference and global gradient information, which can suppress local noise and maintain edge features. The brightness weight calculation equation takes into account the difference between the pixel and the local mean and the brightness consistency of adjacent images at the same time, which helps to deal with the brightness jump at the seam. The comprehensive weight calculation equation adopts the power function combination form, and the contribution of each component can be flexibly controlled by adjusting the exponential term. The weight component decomposition matrix decomposes the weight into two parts, stable and variable, through linear transformation, which is convenient for optimization. The final weight calculation equation adopts the weighted combination form with attenuation, which can effectively suppress high-frequency changes and improve the stability of the splicing result. In general, this step makes full use of various weight calculation methods to provide a high-quality pixel mixing basis for subsequent splicing processing.

[0068] The specific implementation of step S70 is: first, in step 701, the final pixel mixing weight value and the apparent radiance value are obtained. These data will provide a basis for subsequent processing. Next, in step 702, the spliced ​​image data is subjected to brightness balance processing according to the final pixel mixing weight value. This helps to eliminate brightness inconsistencies in the image. Then, in step 703, the image data subjected to brightness balance processing is subjected to color correction according to the apparent radiance value. This ensures that the color representation of the image is more accurate. Then, in step 704, a projection transformation matrix is ​​established. The matrix includes parameters such as scale transformation, shear transformation, translation transformation, and perspective transformation, which can achieve geometric correction of the image. Next, in step 705, the image data subjected to color correction is subjected to projection transformation processing. This step can eliminate geometric distortion in the image. Finally, in step 706, the image data after projection transformation is subjected to coordinate registration. This ensures that the geographical location information in the image is consistent with the actual situation. In general, this step realizes the processing of brightness balance, color correction, geometric correction, and coordinate registration of the image, and prepares for subsequent cutting and output.

[0069] The specific implementation method of step S80 is: first, in step 801, the cutting boundary is determined according to the spatial range parameter. This provides a basis for the subsequent cutting process. Next, in step 802, the image data after the projection transformation is subjected to boundary checking. This helps to identify abnormal areas at the edge of the image. Then, in step 803, the image data after the projection transformation is subjected to cutting according to the cutting boundary. This can obtain the desired target area image. Then, in step 804, the edge area after cutting is smoothed. This can eliminate the jagged effect caused by cutting. Next, in step 805, the image data after cutting is resampled and the boundary is optimized according to the target resolution parameter to obtain the image data after cutting. This step can ensure that the output image meets the required resolution requirements. In general, this step realizes the processing of image cutting, edge smoothing, and resolution resampling, and prepares for the final output.

[0070] The specific implementation method of step S90 is: first, in step 901, the format of the image data after the cropping process is converted. This can ensure that the image data meets the general output format requirements. Next, in step 902, the target layer metadata is generated. These metadata contain key attributes such as geographic information, resolution, and band of the image. Then in step 903, the target layer metadata is associated with the image data after the cropping process. This can ensure the consistency between the metadata and the image data. Then in step 904, the associated data is organized into target layer data. This step realizes the packaging output of image data and metadata. Next, in step 905, the target layer data is compressed and encoded. This can reduce the volume of output data and improve transmission and storage efficiency. Finally, in step 906, the compressed and encoded target layer data is output and saved. In this way, the entire image processing process is completed and the required target layer data is obtained. In general, this step realizes the processing of format conversion, metadata generation, data packaging, and compression encoding of image data, ensuring the quality and efficiency of the final output.

[0071] In order to better understand and implement the present invention, the following is a specific application scenario of the present invention, Example 2: A GIS center is responsible for spatial data management and geographic information services throughout the city. The center has a number of the latest generation of high-resolution remote sensing satellites, and obtains a large amount of satellite image data every day, covering urban areas, suburbs, and some mountainous and rural areas. These image data not only provide basic data support for urban and rural planning, environmental monitoring, etc., but also provide citizens with online map services and spatial query functions.

[0072] In order to effectively manage and efficiently utilize these massive satellite image data, the municipal government GIS center introduced the online satellite image clipping and layer generation method proposed in this invention. The method mainly includes the following steps: The first step is to establish a satellite image spatial index database. The specific steps are as follows: First, according to the geographic location coordinates of the image data, a 5-level spatial grid index was established using the quadtree algorithm. As shown in Table 1, the spatial range and number information of each level of grid cells are recorded in the index table, laying the foundation for subsequent data retrieval.

[0073] Table 1 Satellite image spatial grid index table

[0074] Next, the technicians of the GIS Center collected high-resolution satellite image data of the area in the past year through the ground station receiving equipment and pre-processed it. Specifically, it includes: 1) Extract the original data of solar zenith angle, atmospheric irradiance, atmospheric optical thickness, scattering phase function and surface reflectivity from the image data to prepare for subsequent radiation correction.

[0075] 2) The preprocessed image data is partitioned and stored according to the spatial grid, and a corresponding spatial partition table is established to record the image data range, data volume, data format and other information in each grid unit, providing a basis for rapid retrieval of the target area image data.

[0076] 3) Finally, all the above spatial grid information and spatial partition information are updated to the satellite image spatial index database to facilitate subsequent rapid positioning and access to the required image data.

[0077] The second step is to build a multi-level image pyramid and a dynamic memory pool. The details are as follows: 1) Based on experience, the number of levels of the multi-level image pyramid and memory pool is determined to be 8, that is, .according to The calculation formula is meters (original resolution), Meters (minimum resolution), we can get This can meet a variety of application requirements from high resolution to low resolution.

[0078] 2) For each layer of pyramid image, calculate its resolution , as shown in Table 2. This ensures that the resolution relationship between different levels is reasonable.

[0079] Table 2 Multi-level image pyramid resolution

[0080] 3) Based on the multi-level image pyramid, a corresponding dynamic memory pool is established. The formula for allocating the initial capacity of each layer of memory pool is: MB, This ensures that the bottom layer memory capacity is large and the upper layer memory capacity gradually decreases.

[0081] 4) Considering the differences in access frequency and data density of data at different levels, The optimization formula dynamically adjusts the memory capacity, where and Set to This ensures that frequently accessed high-resolution data can be kept in memory.

[0082] 5) During initialization, according to the load balancing equation Calculate the load index of each layer of memory pool, where Set to This ensures that the memory pool is initially loaded evenly.

[0083] 6) Finally, a dynamic memory management mechanism is established. When the memory pool of a certain layer is overloaded, Make dynamic adjustments to replenish or release memory resources to maintain the balance of the overall load.

[0084] Through the above steps, the GIS center has established an efficient multi-level image data organization and dynamic memory management mechanism, laying the foundation for subsequent rapid image processing.

[0085] The third step is to receive the user's layer cutting request and locate the target image data. The specific process is as follows: 1) An urban planning department submitted a layer clipping request to the GIS center, requesting a 1:5000 scale vector base map of a certain area. The request included the spatial range parameters of the target area, such as the upper left corner coordinates (108.25°, 22.25°) and the lower right corner coordinates (108.75°, 22.00°).

[0086] 2) The processing system of the GIS center first parsed the spatial range parameters in the request, and based on the previously established satellite image spatial grid index, quickly located the three grid cells covering the area: 1-1-1-1-2, 1-1-1-1-3, and 1-1-1-1-4.

[0087] 3) At the same time, the system also analyzes the target resolution parameter in the user's request, which is 1:5000, which is equivalent to a field resolution of about 5 meters. Based on this parameter, the required layer scaling ratio is calculated as .

[0088] 4) Finally, based on the above-mentioned target area grid cell information and scaling ratio, the system locates the original image data covering the area from the satellite image spatial index database, preparing for subsequent image processing.

[0089] The fourth step is to read and process the target image data from the multi-level image pyramid and dynamic memory pool. The details are as follows: 1) First, the system calculates the corresponding pyramid level as the 5th level based on the target resolution of 5 meters requested by the user.

[0090] 2) In the multi-level image pyramid, three target image slices of the fifth layer are located, corresponding to the three grid cells mentioned above.

[0091] 3) Then, the system locates the three memory pool layers corresponding to the fifth layer of pyramid image in the dynamic memory pool. If it is found that some slice data has not been cached in the memory, it will be automatically loaded from the hard disk to the corresponding memory layer.

[0092] 4) Next, the system uses the previously established radiation transfer equations to perform radiation correction on the image slice data in the three memory pool layers. Specifically, it includes: Using the solar radiation calculation equation Calculate the incident radiation flux value; use the atmospheric scattering calculation equation Calculate the atmospheric path radiation value; use the surface reflection calculation equation Calculate the surface reflected radiation value; use the sensor to receive the calculation equation Calculate the apparent radiation brightness value; use the radiation component decomposition calculation matrix to decompose the apparent radiation brightness value into stable components and variable components to obtain the radiation correction coefficient matrix.

[0093] 5) Then, the system uses the previously established weight calculation equations to adaptively stitch the image slice data in the three memory pool layers. Specifically, it includes: Using distance weight calculation equation Calculate the initial distance weight; use the gradient weight calculation equation Calculate the initial gradient weight; use the brightness weight calculation equation Calculate the initial brightness weight; use the comprehensive weight calculation equation Calculate the initial pixel mixing weight; use the weight component decomposition calculation matrix to decompose the initial weight into stable components and variable components, and finally Calculate the final pixel blending weights.

[0094] Through the above-mentioned radiation correction and pixel stitching processing, the system generates high-quality image data covering the target area.

[0095] The fifth step is to perform geometric correction and cropping on the stitched image data. The specific steps are as follows: 1) First, the system determines the coordinates of the four vertices of the cropping area according to the spatial range parameters requested by the user: (108.25°, 22.25°), (108.25°, 22.00°), (108.75°, 22.25°), (108.75°, 22.00°). And establishes the boundary indication function to identify the cropping area.

[0096] 2) Then, the system establishes the projection transformation matrix ,in , to achieve the scale transformation and shear transformation of the image. Next, the spliced ​​image data is projected. ,in , the final coordinates are .

[0097] 3) In order to further optimize the projection results, the system uses four ground control points, (108.25°, 22.20°), (108.25°, 22.05°), (108.70°, 22.25°), and (108.70°, 22.00°), and iteratively fine-tunes the projection transformation matrix parameters so that the reprojection error of the control point coordinates is less than 0.5 meters.

[0098] 4) Finally, the system determines the cutting area boundaries according to the previously determined , the image data after projection transformation was cropped. At the same time, Gaussian smoothing filter was used on the edge area after cropping. , eliminating the jagged effect caused by cutting.

[0099] Through the above-mentioned geometric correction and cropping processing, the system generates a 1:5000 scale vector base map layer covering the user-specified area with good geometric accuracy and edge smoothness. Figure 2 As shown, a schematic diagram of the control point distribution and reprojection error range is shown.

[0100] The sixth step is to convert the format, generate metadata and compress the final layer data. The details are as follows: 1) First, the system converts the cropped image data into a standard GIS raster data format, such as GeoTIFF, to ensure that the data can be recognized and used by common GIS software.

[0101] 2) Then, the system generates corresponding metadata based on the image's geographic range, resolution, band and other information, and associates it with the image data to form a complete layer data package.

[0102] 3) Finally, the system performs lossy compression encoding on the layer data. The specific algorithm used is: ,in is the compressed image, is the original image, is the transform domain coefficient. Through this sparse optimized encoding method, the volume of layer data can be compressed to about 30% of the original, and the image quality index Still able to reach a higher level.

[0103] So far, the GIS center has successfully generated a 1:5000 scale vector base map layer for the urban planning department. The layer data has been effectively managed, quickly pre-processed and customized for output. It is not only small in size and highly efficient in transmission, but also has achieved high standards in terms of geometric accuracy, radiation correction, visual effects and other aspects, fully meeting the needs of users.

[0104] In addition, the GIS center can also flexibly generate customized layer data of various scales and coverage ranges according to user needs for different application scenarios, such as environmental monitoring, urban planning, emergency response, etc., giving full play to the advantages of the method of the present invention.

[0105] It should be noted that the explanations of the variables involved in the present invention are shown in Table 3 and Table 4.

[0106] Table 3 Variable explanation table

[0107] Table 4 Variable Explanation Table-2

[0108] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for online satellite image cropping and layer generation, characterized in that: include: By establishing a satellite image spatial index database, a multi-level image pyramid and a multi-level image memory pool, the satellite image data to be processed is partitioned and stored and hierarchically managed. Based on the parameters in the layer clipping request, the image slice data to be processed is obtained from the target pyramid level and allocated to the corresponding target memory pool level. The radiation transfer equations are used for atmospheric correction and the weight calculation equations are used for edge feathering, and finally the target layer data is generated.

2. The method for online satellite image cropping and layer generation according to claim 1, characterized in that: The establishment of the satellite image spatial index database is specifically as follows: partitioning and storing the satellite image data to be processed, wherein the satellite image data to be processed includes original data of solar zenith angle, original data of external atmospheric irradiance, original data of atmospheric optical thickness, original data of scattering phase function, and original data of surface reflectivity.

3. The method for online satellite image cropping and layer generation according to claim 2, characterized in that: The establishment of a multi-level image pyramid and a multi-level image memory pool is specifically as follows: a multi-level image pyramid is generated for the satellite image data to be processed according to a preset image zoom level, and the multi-level image pyramid is stored in a system cache, and a multi-level image memory pool is established at the same time, wherein the number of levels of the multi-level image memory pool corresponds one-to-one to the number of levels of the multi-level image pyramid.

4. The method for online satellite image cropping and layer generation according to claim 3, characterized in that: The method based on the parameters in the layer clipping request specifically includes: receiving a layer clipping request, and locating target satellite image data from the satellite image spatial index database according to the spatial range parameter and target resolution parameter included in the layer clipping request.

5. The method for online satellite image cropping and layer generation according to claim 4, characterized in that: The obtaining of the image slice data to be processed from the target pyramid level is specifically as follows: selecting a corresponding target pyramid level in the multi-level image pyramid according to the target resolution parameter, obtaining the image slice data to be processed in the target pyramid level, and allocating the image slice data to be processed to a target memory pool level in the multi-level image memory pool corresponding to the target pyramid level.

6. The method for online satellite image cropping and layer generation according to claim 5, characterized in that: The radiation transfer equation group includes solar radiation calculation equations, atmospheric scattering calculation equations, surface reflection calculation equations, sensor reception calculation equations, and radiation component decomposition calculation matrices, which are used to perform atmospheric correction processing on the image slice data to be processed in the target memory pool level to obtain corrected image slice data.

7. The method for online satellite image cropping and layer generation according to claim 6, characterized in that: The weight calculation equation group includes a distance weight calculation equation, a gradient weight calculation equation, a brightness weight calculation equation, a comprehensive weight calculation equation, and a weight component decomposition calculation matrix, which are used to dynamically splice the corrected image slice data and perform edge feathering processing in the target memory pool level.

8. The method for online satellite image cropping and layer generation according to claim 7, characterized in that: The specific method of generating the target layer data is as follows: performing projection transformation processing on the spliced ​​image data according to the final pixel mixing weight value output by the weight calculation equation group and the apparent radiation brightness value output by the radiation transfer equation group, performing cropping processing on the image data after the projection transformation according to the spatial range parameter, and outputting the cropped image data as the target layer data.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the method for online satellite image clipping and layer generation as described in any one of claims 1 to 8.

10. A system for online satellite image cropping and layer generation, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

Images