Construction method and device for accuracy lookup table of regional network adjustment results of remote sensing images

Abstract

Embodiments of the present disclosure provide a method and apparatus for constructing an accuracy lookup table for aerial triangulation results of remote sensing images, which are applied to the field of remote sensing application technologies. The method includes calculating the area and proportion of each surface coverage type and each terrain type within the aerial triangulation network respectively, collecting a sufficient number of evenly distributed detection points required for calculating the accuracy of the aerial triangulation results of remote sensing images within the survey area; designing a sequence of the number and positions of sample detection points corresponding to each surface coverage type on each terrain type, calculating the sequence of mean square errors corresponding to each number of sample detection points, statistically analyzing the relationship between the accuracy of the triangulation results, the area and proportion of terrain and land types, the number and distribution of detection points, obtaining the minimum number of inspection points required for checking the accuracy of the aerial triangulation results through marginal effect, and constructing an accuracy lookup table for the aerial triangulation results of remote sensing images. In summary, it is possible to avoid the uncertainty in the detection of aerial triangulation results caused by differences in the number of selected points and uneven position distributions.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing, in particular to the technical field of remote sensing applications, and specifically to a method and device for constructing an accuracy lookup table for the results of block adjustment of remote sensing images. Background Art

[0002] The quality inspection of surveying and mapping products is an important link in controlling the quality of surveying and mapping products. Among them, there are corresponding national or industry standards for the quality inspection of 4D surveying and mapping products such as Digital Orthophoto Map (DOM), Digital Line Graphic (DLG), Digital Elevation Model (DEM), and Digital Raster Graphic (DRG). The quality inspection of single-scene orthorectified image products, which are non-4D products, can refer to that of 4D products.

[0003] However, there are currently no technical regulations and standards that can be relied on or referenced for the results of block adjustment of remote sensing images, which leads to uncertainty in the quality inspection results of block adjustment results. At the same time, in the scenario of large-scale mapping overseas, there is a lack of a complete and reliable data source of geometric feature reference points. This brings great challenges to the mapping work and the verification work of mapping results.

[0004] Due to the diversity of open-source data, there are significant differences in accuracy, timeliness, and resolution, etc. Therefore, there are certain difficulties in mining such data information, and there is currently no complete set of information mining means. It is an urgent problem to collect and organize the existing open-source data to prepare a reliable reference information source.

[0005] In summary, for the quality inspection of the accuracy of the results of block adjustment of remote sensing images, there are mainly two problems in the existing technology: one is that the typicality and representativeness of the selected point positions cannot be determined; the other is that it cannot be determined whether the number of detection points can meet the requirements of the accuracy inspection of block adjustment. Summary of the Invention

[0006] The present disclosure provides a method and device for constructing an accuracy lookup table for the results of block adjustment of remote sensing images.

[0007] According to a first aspect of the present disclosure, there is provided a method for constructing an accuracy lookup table for the results of block adjustment of remote sensing images. The method includes:

[0008] Obtaining surface cover type data and terrain elevation data in the survey area of the block adjustment of remote sensing images;

[0009] According to the surface cover type data and the terrain elevation data, calculate the area and proportion of each surface cover type on each terrain type within the survey area for block adjustment respectively;

[0010] According to the surface cover type data and the terrain elevation data, collect enough and evenly distributed detection points within the survey area for calculating the accuracy of the block adjustment results of remote sensing images;

[0011] Design a sequence of the number and position of sample detection points corresponding to each surface cover type on each terrain type, and calculate the sequence of mean square errors corresponding to each number of sample detection points;

[0012] Based on the sequence of mean square errors of sample detection points corresponding to each surface cover type on each terrain type, statistically analyze the relationship between the accuracy of adjustment results, the area and proportion of terrain types, as well as the number and distribution of detection points. Obtain the minimum number of inspection points required for checking the accuracy of block adjustment results through marginal effect, and construct a lookup table for the accuracy of block adjustment results of remote sensing images.

[0013] For the above aspects and any possible implementation manners, a further implementation manner is provided. The design of the sequence of the number and position of sample detection points corresponding to each surface cover type on each terrain type, and the calculation of the sequence of mean square errors corresponding to each number of sample detection points include:

[0014] Based on a preset detection point distribution algorithm, according to the area and proportion of each surface cover type on each terrain type within the survey area for block adjustment, as well as the number and distribution of detection points, determine the number and position of sample detection points on each surface cover type on each terrain type;

[0015] According to the number and position of sample detection points on each surface cover type on each terrain type, calculate the sequence of mean square errors of sample detection points corresponding to each surface cover type on each terrain type. The sequence of mean square errors of sample detection points corresponding to each surface cover type on each terrain type records the mean square error information calculated under different strategies in the form of a list.

[0016] For the above aspects and any possible implementation manners, a further implementation manner is provided. The statistical analysis of the relationship between the accuracy of adjustment results, the area and proportion of terrain types, as well as the number and distribution of detection points based on the sequence of mean square errors of sample detection points corresponding to each surface cover type on each terrain type, obtaining the minimum number of inspection points required for checking the accuracy of block adjustment results through marginal effect, and constructing a lookup table for the accuracy of block adjustment results of remote sensing images includes:

[0017] Using the marginal effect of the error in the checkpoints, linearly fit the error sequence of the sample checkpoints corresponding to each surface cover type on each terrain type with the number of sample checkpoints as the horizontal axis and the error as the vertical axis, and calculate the stationary points; wherein, the error sequence of the sample checkpoints corresponding to each surface cover type on each terrain type includes the error sequence of a single surface cover type corresponding to a single terrain type or the error sequence of a single terrain type corresponding to a single surface cover type.

[0018] According to the stationary points corresponding to each surface cover type on each terrain type, calculate the number of sample checkpoints corresponding to each stationary point to obtain the minimum number of inspection points.

[0019] Determine the influence weights of the terrain type and the surface cover type according to the number of sample checkpoints corresponding to each stationary point.

[0020] According to the influence weights, the area and proportion in the survey area of the block adjustment of the remote sensing image of each surface cover type on each terrain type, as well as the number and distribution of the checkpoints, obtain the corresponding minimum number of checkpoints, and construct a lookup table for the accuracy of the block adjustment results of the remote sensing image.

[0021] For the aspects and any possible implementation manners as described above, a further implementation manner is provided. The obtaining of the surface cover type data and the terrain elevation data in the survey area of the block adjustment of the remote sensing image includes:

[0022] According to the resolution and accuracy of the adjustment results in the survey area of the block adjustment of the remote sensing image, obtain the initial surface cover type data and the initial terrain elevation data in the survey area.

[0023] Sort the initial surface cover type data and the initial terrain elevation data in different scale orders according to the type, resolution and accuracy of the obtained data.

[0024] Rename the sorted initial surface cover type data and the initial terrain elevation data according to the preset rules, and store them in the database according to the pixel position to complete the construction of the geographic condition background library for the block adjustment.

[0025] Obtain the surface cover type data and the terrain elevation data in the survey area of the block adjustment of the remote sensing image from the geographic condition background library for the block adjustment.

[0026] For the aspects and any possible implementation manners as described above, a further implementation manner is provided. The calculating of the area and proportion of each surface cover type on each terrain type in the survey area of the block adjustment according to the surface cover type data and the terrain elevation data includes:

[0027] Normalize the surface cover type data and the terrain elevation data according to a preset resolution, where the normalization is to construct a complete pyramid data body of a regional network adjustment geographical condition background library with multi-level continuity of resolution and accuracy.

[0028] According to the normalized surface cover type data and terrain elevation data, calculate the area and proportion of each surface cover type on each terrain type in the survey area of the regional network adjustment respectively.

[0029] In the aspect and any possible implementation manner as described above, a further implementation manner is provided. The detection points for collecting a sufficient number of evenly distributed sample detection points required for calculating the accuracy of the regional network adjustment of remote sensing images in the survey area according to the surface cover type data and the terrain elevation data include:

[0030] Based on a preset detection point extraction model, collect the number and distribution of sample detection points required to determine the accuracy of the regional network adjustment of remote sensing images; the sample detection points are reliable and sufficient in number.

[0031] In the aspect and any possible implementation manner as described above, a further implementation manner is provided. The surface cover type data includes cultivated land, forest land, grassland, shrub land, wetland, water body, tundra, artificial surface, bare land, glacier, and permanent snow; the terrain elevation data includes terrain categories, and the terrain categories include high mountains, mountains, hills, and flatlands.

[0032] According to the second aspect of the present disclosure, a device for constructing a look-up table for the accuracy of regional network adjustment of remote sensing images is provided. The device includes:

[0033] An acquisition module for acquiring surface cover type data and terrain elevation data in the survey area of the regional network adjustment of remote sensing images;

[0034] A processing module for calculating the area and proportion of each surface cover type on each terrain type in the survey area of the regional network adjustment respectively according to the surface cover type data and the terrain elevation data;

[0035] The processing module is further configured to collect, within the survey area, detection points of sample detection points sufficient in number and evenly distributed for calculating the accuracy of the regional network adjustment of remote sensing images according to the surface cover type data and the terrain elevation data;

[0036] The processing module is further configured to design a sequence of the number and positions of sample detection points corresponding to each surface cover type on each terrain type, and calculate a sequence of mean square errors corresponding to each number of sample detection points;

[0037] A generation module, configured to, based on the error sequences of the sample detection points corresponding to each surface coverage type on each terrain type, statistically analyze the relationship between the adjustment result accuracy, the area and proportion of the terrain and land types, as well as the number and distribution of the detection points, obtain the minimum number of checkpoints required for checking the accuracy of the block adjustment result through marginal effect, and construct a lookup table for the accuracy of the block adjustment result of the remote sensing image.

[0038] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, where a computer program is stored on the memory, and when the processor executes the program, the method as described above is implemented.

[0039] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method as described above is implemented.

[0040] A method and apparatus for constructing a lookup table for the accuracy of the block adjustment result of a remote sensing image provided by an embodiment of the present application can obtain the surface coverage type data and terrain elevation data in the survey area of the block adjustment of the remote sensing image; then, according to the surface coverage type data and terrain elevation data, calculate the area and proportion of each surface coverage type on each terrain type in the survey area of the block adjustment respectively; then, according to the surface coverage type data and terrain elevation data, collect detection points with a sufficient number and uniform distribution within the survey area for calculating the accuracy of the block adjustment result of the remote sensing image; then, design a sequence of the number and position of the sample detection points corresponding to each surface coverage type on each terrain type, and calculate the mean square error sequence corresponding to each number of sample detection points; then, based on the mean square error sequences of the sample detection points corresponding to each surface coverage type on each terrain type, statistically analyze the relationship between the adjustment result accuracy, the area and proportion of the terrain and land types, as well as the number and distribution of the detection points, obtain the minimum number of checkpoints required for checking the accuracy of the block adjustment result through marginal effect, and construct a lookup table for the accuracy of the block adjustment result of the remote sensing image; based on this, the relationship between the accuracy of the block adjustment result, the point position, the surface coverage and terrain conditions, and the number of points can be established by using the method for constructing the lookup table for the accuracy of the block adjustment result of the remote sensing image, avoiding the uncertainty of the detection of the block adjustment result caused by the difference in the number of selected points and the uneven position distribution.

[0041] It should be understood that the content described in the summary of the invention is not intended to limit the key or important features of the embodiments of the present disclosure, nor to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In conjunction with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not limit the present disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, where:

[0043] Figure 1 A flowchart showing a method for constructing an accuracy lookup table for the adjustment results of a remote sensing image block adjustment according to an embodiment of the present disclosure is shown;

[0044] Figure 2 A block diagram showing a device for constructing an accuracy lookup table for the adjustment results of a remote sensing image block adjustment according to an embodiment of the present disclosure is shown;

[0045] Figure 3 A block diagram showing an exemplary electronic device capable of implementing the embodiments of the present disclosure is shown. Detailed implementation manners

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0047] In addition, the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0048] In the present disclosure, a method for constructing an accuracy lookup table for the adjustment results of a remote sensing image block adjustment can be used to establish the relationship between the accuracy of the block adjustment results, the point position, the surface coverage and terrain conditions, and the number of points, avoiding the uncertainty in the detection of the block adjustment results caused by the difference in the number of selected points and the uneven distribution of positions.

[0049] Figure 1 A flowchart showing a method 100 for constructing an accuracy lookup table for the adjustment results of a remote sensing image block adjustment according to an embodiment of the present disclosure is shown.

[0050] In block 110, obtain the surface coverage type data and terrain elevation data in the survey area of the remote sensing image block adjustment.

[0051] At block 120, based on the surface cover type data and the terrain elevation data, calculate the area and proportion of each surface cover type on each terrain type within the survey area for block adjustment.

[0052] At block 130, based on the surface cover type data and the terrain elevation data, collect a sufficient number of evenly distributed checkpoints within the survey area for calculating the accuracy of the block adjustment results of the remote sensing images.

[0053] At block 140, design a sequence of the number and positions of the checkpoints corresponding to each surface cover type on each terrain type, and calculate the sequence of mean square errors corresponding to each number of checkpoints.

[0054] At block 150, based on the sequence of mean square errors of the checkpoints corresponding to each surface cover type on each terrain type, statistically analyze the relationship between the accuracy of the adjustment results, the area and proportion of the terrain and land cover types, as well as the number and distribution of the checkpoints. Obtain the minimum number of checkpoints required for checking the accuracy of the block adjustment results through marginal effects, and construct a lookup table for the accuracy of the block adjustment results of the remote sensing images.

[0055] At block 110, the surface cover type data and the terrain elevation data within the survey area for the block adjustment of the remote sensing images can be obtained by establishing a block adjustment geographical condition background database and retrieving them from the block adjustment geographical condition background database.

[0056] In some embodiments, various types of geographical condition data such as the surface cover type data and the terrain elevation data within the survey area for the block adjustment of the remote sensing images are collected according to the regional scope of the block adjustment of the remote sensing images. A block adjustment geographical condition background database is established through comprehensive analysis of the resolution and accuracy of the multi-resolution and multi-type data combined with the block adjustment data. In the block adjustment geographical condition background database, the surface cover type data and the terrain elevation data can be stored according to the pixel position.

[0057] In some embodiments, the above-mentioned obtaining of the surface cover type data and the terrain elevation data within the survey area for the block adjustment of the remote sensing images includes:

[0058] Obtain the initial surface cover type data and the initial terrain elevation data within the survey area according to the resolution and accuracy of the adjustment results of the block adjustment of the remote sensing images;

[0059] Sort the initial surface cover type data and the initial terrain elevation data in different scales according to the type, resolution, and accuracy of the obtained data;

[0060] Rename the sorted initial surface cover type data and the initial terrain elevation data according to the preset rules, and store them in the database according to the pixel position to complete the construction of the block adjustment geographical condition background database;

[0061] Obtain the surface cover type data and topographic elevation data within the survey area for the block adjustment of remote sensing images from the regional network adjustment geographical condition background database.

[0062] In some embodiments, according to the basic conditions of the survey area such as the regional network adjustment area range, the resolution and accuracy of the adjustment data, etc., collect the geographical condition data such as the surface cover type data and topographic elevation data within the survey area to be quality inspected. During the collection process, try to collect data with better resolution and accuracy as a reference, that is, the initial surface cover type data and initial topographic elevation data within the survey area.

[0063] In some embodiments, the initial surface cover type data with different resolutions can be selected according to actual needs. For example, select the IGBP (International Geosphere Biosphere Programme) land cover classification scheme provided by the MODIS (Moderate Resolution Imaging Spectroradiometer) land cover type product, with a resolution of 500 meters and including 17 land types, which is suitable for the needs where the requirements for land types are not precise; select the Global 30-meter Surface Cover (GlobeLand30) remote sensing mapping dataset, with a resolution of 30 meters and including 10 land types, which is suitable for the needs where the requirements for land types are more refined.

[0064] In some embodiments, the initial topographic elevation data can use open-source datasets. The SRTM (Shuttle Radar Topography Mission) data globally includes datasets with different resolutions of 30 meters and 90 meters, and its coverage range is between 56 degrees south latitude and 61 degrees north latitude. For the initial topographic elevation data in high latitudes, other types of datasets can be selected, such as ASTER GDEM (Advanced Spaceborne Theemal Emission and Reflection Radiometer Global Digital Elevation Model), and the latitude coverage range can be up to 83 degrees north latitude.

[0065] In some embodiments, for the convenience of subsequent operations, the obtained data can be sorted and analyzed. Specifically, the various geographical condition data obtained can be sorted in ascending order at different scales according to different data types, resolutions, and accuracies, match the accuracy of the initial surface cover type data and the initial topographic elevation data, and conduct comprehensive analysis of the multi-source data. When necessary, the resolution can be normalized to the same scale in the form of building a pyramid to achieve the corresponding relationship of the multi-source data at the same scale.

[0066] In some embodiments, the sorted and analyzed multi-source geographic condition data can be renamed according to a preset rule and stored in a database for subsequent use. By naming, the corresponding relationships between multi-source data at different scales can be effectively distinguished, laying a foundation for subsequent applications. Among them, the preset rule can be set according to the actual needs of users.

[0067] For example, the naming method can choose "dataset category_data source name_resolution_data accuracy", and the accuracy can be omitted. For example, the land cover type data can be named "LAND USE CLASSIFICATION_GlobeLand30_30m", which represents the GlobeLand30 land cover type product with a spatial resolution of 30 meters; the terrain elevation data can be named "DEM_SRTM_30m_10m", which represents the SRTM (Shuttle Radar Topography Mission) terrain elevation data with a spatial resolution of 30 meters and an accuracy of 10 meters.

[0068] In block 120, by analyzing the regional network adjustment regional geographic condition background library, according to the land cover type data and terrain elevation data, the area and proportion of each land cover type on each terrain type in the survey area of the regional network adjustment can be calculated respectively.

[0069] Specifically, the resolution of the required scale can be determined according to the quality inspection requirements of the regional network adjustment data. The land cover type data and terrain elevation data are normalized spatially. Then, using the normalized land cover type data and terrain elevation data, the area and proportion of each land cover type and each terrain type in the survey area are calculated.

[0070] In some embodiments, the above-mentioned calculating the area and proportion of each land cover type on each terrain type in the survey area of the regional network adjustment according to the land cover type data and terrain elevation data respectively includes:

[0071] According to the preset resolution, the land cover type data and terrain elevation data are normalized. The normalization process is to construct a complete pyramid data body of the regional network adjustment geographic condition background library with multi-level continuity of resolution and accuracy;

[0072] According to the normalized land cover type data and terrain elevation data, the area and proportion of each land cover type on each terrain type in the survey area of the regional network adjustment are calculated respectively.

[0073] In some embodiments, after the data is collected and organized into the regional network adjustment geographical condition background database, the existing results can be analyzed with multi-scale data sources. If there are clear scale requirements for the data to be inspected, namely the land cover type data and terrain elevation data, such as same-precision quality inspection, high-precision quality inspection, etc., then the scale that best meets the requirements is extracted, and further analysis is carried out on whether there are differences in resolution and precision among the multi-source geographical condition data at this scale. If there are differences, further normalization processing is required to unify the indicators of the multi-source geographical condition data at this scale; if there are no clear scale requirements, then data analysis is carried out on all the multi-source geographical condition data at multiple scales.

[0074] For example, if the scale requirement of the data to be inspected is same-precision quality inspection with a spatial resolution of 30 meters, then the GlobeLand30 land cover type product with a 30-meter spatial resolution, the SRTM terrain elevation data with a 30-meter spatial resolution and an accuracy of 10 meters, and the ASTER GDEM terrain elevation data with a 30-meter spatial resolution and an accuracy of 7 - 14 meters can be directly used, while the MODIS land cover type product with a 500-meter spatial resolution needs to be resampled to a scale with a spatial resolution of 30 meters to achieve consistency in spatial scale, that is, normalization processing. If the scale requirement of the data to be inspected is high-precision quality inspection with a spatial resolution of 30 meters, then data with a higher spatial resolution needs to be collected to achieve high-precision quality inspection, such as collecting land cover type data and terrain elevation data products with a spatial resolution of 10 meters.

[0075] In some embodiments, multi-dimensional extraction is performed on the multi-source geographical condition data at the selected scale, that is, the normalized land cover type data and terrain elevation data, for example, at levels such as area and proportion. The terrain elevation data and land cover type data included in the regional network adjustment geographical condition background database can be distinguished according to the terrain categories of the terrain elevation data, such as high mountains, mountains, hills, and flatlands, at the same scale, and area and proportion statistics are carried out. Among them, the terrain categories can be classified according to the slope of the terrain elevation data, or existing terrain classification vector data can be collected and directly used. Then, area and proportion statistics are carried out according to various types of the GlobeLand30 land cover type data, such as cultivated land, forest land, grassland, shrub land, wetland, water body, tundra, artificial surface, bare land, glacier, and permanent snow cover. The areas and proportions of each land cover type on each terrain type in the survey area of the regional network adjustment are shown in Table 1.

[0076] Table 1: Areas and Proportions of Each Land Cover Type on Each Terrain Type in the Survey Area of the Regional Network Adjustment

[0077]

[0078] At frame 130, the number and distribution selection scheme of position accuracy detection points can be designed under the composite multi-scene conditions of various terrain types and surface coverage types. According to the designed scheme, the detection points are first selected in an automatic computer manner. After automatic point selection, manual verification is carried out to eliminate the pseudo-detection points automatically selected, that is, the points with large errors. At the same time, the detection points that do not meet the design requirements are supplemented. Finally, reliable and sufficient detection points are obtained to complete the detection of the sample detection points required for obtaining the accuracy of the regional network adjustment results of the computed remote sensing image.

[0079] It should be noted that in the process of constructing the accuracy lookup table for the regional network adjustment results of remote sensing images, surface coverage and terrain data can also be directly obtained for the selection of sample detection points. The establishment and analysis of the regional network adjustment geographical condition background library belong to data preprocessing to further accurately define the relationship between the accuracy of the regional network adjustment results and the point position, surface coverage, terrain conditions, and the number of points, so as to further avoid the uncertainty in the detection of the regional network adjustment results caused by differences in the number of selected points and uneven position distribution.

[0080] In some embodiments, according to the surface coverage type data and terrain elevation data, the detection points for the sample detection points required for obtaining the accuracy of the regional network adjustment results of the computed remote sensing image collected within the survey area and having sufficient quantity and uniform distribution include:

[0081] Based on a preset detection point extraction model, the detection points for the sample detection points required for determining the accuracy of the regional network adjustment results of the remote sensing image are collected, and the detection points are reliable and sufficient in number.

[0082] In some embodiments, according to the analysis of various terrain categories and surface coverage composite multi-scene conditions within the survey area, implementation schemes for selecting position accuracy detection points with reasonable data volume and distribution are designed for various combinations that appear. The design of the scheme needs to comprehensively consider the various surface coverages on each terrain category, design the relationship between the terrain category and the surface coverage according to actual needs, and appropriately make choices for unnecessary combinations.

[0083] In some embodiments, the non-existent terrain categories and surface coverage types are deleted according to the actual situation within the survey area; when there are many surface coverage types, they can also be merged according to actual needs, such as merging forest land, grassland, shrub land, and wetland into vegetation.

[0084] In some embodiments, according to the designed point selection scheme, a suitable software is used to automatically select the check points, and the automatically selected check points are used as alternative data sources. The computer can greatly save time costs and is conducive to quickly completing the work tasks.

[0085] It should be noted that the above software for automatically extracting checkpoints can be selected according to the actual situation. At the same time, the number of checkpoints should be selected as many as possible compared with the design scheme to prepare for the subsequent elimination of incorrect checkpoints.

[0086] In some embodiments, when extracting checkpoints, existing mature commercial software such as ENVI, ARCGIS, GXL, etc. can be used, or the required algorithms can be called through various programming tools to obtain checkpoints.

[0087] In some embodiments, the method of manually verifying the accuracy of the checkpoints automatically selected by the computer can be adopted to eliminate the checkpoints that do not meet the requirements. On this basis, the checkpoints with deviations from the design scheme are supplemented to ensure an adequate number of checkpoints.

[0088] In some embodiments, various strategies can be adopted in the manual verification stage to confirm the accuracy of the checkpoints. For example, the errors of the checkpoints are arranged in ascending or descending order, and the points with larger errors are verified first; a certain proportion of checkpoints are randomly selected for medium error calculation, and the differences between multiple medium errors are compared to judge the reliability of the checkpoints, etc. Through this step, plane position checkpoints with sufficient quantity, reasonable distribution, and reliable accuracy can be collected.

[0089] In some embodiments, the medium error calculation formula is as follows:

[0090]

[0091] Among them, S represents the medium error, N represents the number of checkpoints, x and y represent the plane position coordinates of the data to be checked, and X and Y are the position coordinates of the corresponding homologous checkpoints of the reference image and the data to be checked.

[0092] In block 140, the number of sample detection points can be statistically associated with the terrain category and surface cover background information, a stratified scheme for sample detection points is designed, and the detection points are stratified according to the stratified scheme using a random algorithm. Among them, the stratified scheme should consider the number of sample detection points and their distribution in different terrain categories and surface covers in the survey area. The stratified sample detection points are calculated for errors using the medium error formula, and the error results are statistically associated with the terrain category and surface cover.

[0093] In some embodiments, the above-mentioned sequence of calculating the number and position of sample detection points corresponding to each surface cover type on each terrain type, and calculating the medium error sequence corresponding to each number of sample detection points includes:

[0094] Based on a preset detection point distribution algorithm, according to the area and proportion of the regional network adjustment in the survey area of each surface cover type on each terrain type, as well as the number and distribution of detection points, determine the number and position of sample detection points on each surface cover type on each terrain type;

[0095] According to the number and location of sample detection points on each surface coverage type for each terrain type, calculate the mean error sequence of the sample detection points corresponding to each surface coverage type for each terrain type. The mean error sequence of the sample detection points corresponding to each surface coverage type for each terrain type records the mean error information calculated under different strategies in the form of a list.

[0096] In some embodiments, during the process of designing the hierarchical scheme of sample inspection points, a random algorithm, i.e., a preset detection point distribution algorithm, can be used to stratify the sample detection points according to the hierarchical scheme. The hierarchical scheme should consider the number of sample detection points and the distribution of sample detection points on different terrain categories and surface coverages in the survey area, ensuring that there are a sufficient number of points for each terrain category and surface coverage type required for quality inspection in the distribution of sample detection points for subsequent analysis.

[0097] For example, during this process, the terrain category can be used as the first-level classification, dividing the survey area into four parts: flat land, hilly land, mountain land, and alpine land. Then, the surface coverage category is introduced into each terrain classification, and different numbers of sample detection points are selected in different surface coverage type areas based on the four terrain categories for mean error calculation.

[0098] In some embodiments, the stratified sample detection points can be used to calculate the error using the mean error formula, and the hierarchical scheme is used to perform correlation statistics on the plane geometric position accuracy results, terrain categories, and surface coverages.

[0099] For example, under the condition of fully considering the geographical conditions of terrain categories and surface coverages, the mean errors calculated from different numbers of sample detection points are statistically analyzed. The number of sample detection points can be selected according to an arithmetic progression according to the actual situation, such as 5, 10, 15, 20..., and it is required that these different numbers of sample detection points be evenly distributed in the survey area. The mean error information calculated under different strategies is recorded in the form of a list.

[0100] Specifically, first, the error results are obtained according to the terrain category classification when introducing the surface coverage type, which can be listed in four tables classified by terrain category. Each table represents a terrain category, and the mean error conditions of different numbers of sample detection points under different surface coverage types are statistically analyzed. For example, if it is necessary to statistically analyze the sample inspection points with different numbers under different terrain conditions for four surface coverage categories, the table shown in Table 2 can be used for statistics, and each terrain category lists a table for independent statistics.

[0101] Table 2: Mean Errors of Sample Inspection Points with Different Numbers under Different Terrain Conditions for Four Surface Coverage Categories

[0102]

[0103] At block 150, based on the error sequences of the sample detection points corresponding to each surface coverage type on each terrain type, the relationship between the adjustment result accuracy, the area and proportion of the terrain and land types, as well as the number and distribution of the detection points is statistically analyzed. The minimum number of inspection points required for checking the accuracy of the regional network adjustment results is obtained through marginal effect, and a lookup table for the accuracy of the regional network adjustment results of remote sensing images is constructed.

[0104] In some embodiments, the above-mentioned statistical analysis of the relationship between the adjustment result accuracy, the area and proportion, as well as the number and distribution of the detection points based on the error sequences of the sample detection points corresponding to each surface coverage type on each terrain type, and the construction of the lookup table for the accuracy of the regional network adjustment results of remote sensing images include:

[0105] Using the marginal effect of the error of the detection points, linear fitting is performed on the error sequences of the sample detection points corresponding to each surface coverage type on each terrain type with the number of sample detection points as the horizontal axis and the error as the vertical axis, and the stationary points are calculated; among them, the error sequences of the sample detection points corresponding to each surface coverage type on each terrain type include the error sequence of a single surface coverage type corresponding to a single terrain type or the error sequence of a single terrain type corresponding to a single surface coverage type.

[0106] According to the stationary points corresponding to each surface coverage type on each terrain type, the number of sample detection points corresponding to each stationary point is calculated to obtain the minimum number of inspection points.

[0107] According to the number of sample detection points corresponding to each stationary point, the influence weights of the terrain type and the surface coverage type are respectively determined.

[0108] According to the influence weights, the area and proportion in the survey area of the regional network adjustment of the remote sensing images corresponding to each surface coverage type on each terrain type, as well as the number and distribution of the detection points, the corresponding minimum number of detection points is obtained, and a lookup table for the accuracy of the regional network adjustment results of remote sensing images is constructed.

[0109] In some embodiments, the errors calculated from different numbers of sample detection points under multi-source geographical conditions of different terrain categories and surface coverages can be analyzed, that is, linear fitting is performed on the error sequence of a single surface coverage corresponding to a single terrain category or the error sequence of a single terrain category corresponding to a single surface coverage with the number of detection points as the horizontal axis and the error as the vertical axis, and the stationary points are calculated. The number of detection points corresponding to the error at the current position is calculated through the stationary points and recorded. The stationary points are calculated for each combination in different situations, and the number of inspection points corresponding to the stationary points under each combination is statistically analyzed.

[0110] In some embodiments, the linear fitting can be performed in the form of polynomial fitting, and the polynomial fitting formula is as follows:

[0111]

[0112] Among them, m and n represent the number of points and the mean square error values corresponding to the horizontal and vertical coordinates respectively, i is the highest power of the polynomial, the higher the degree, the more accurate the curve, but the greater the computational amount. A reasonable polynomial needs to be selected, and the polynomial coefficients are calculated through the existing data , so as to obtain the required equation.

[0113] In some embodiments, according to the above calculation results, it can be analyzed whether the influencing factor affecting the final mean square error is the terrain category or the surface cover, the influence weights of the two geographical condition data on the final result are obtained, a plane precision detection lookup table is obtained, and the number of selected points in the final survey area also needs to be combined with the weighted values of various classifications in the survey area of the factor with greater influence, and finally ensure the determination of a reasonable number of inspection points.

[0114] For example, analyze the relationship between the number of inspection points and the precision according to the terrain category and the surface cover respectively, compare the influence of the two geographical conditions on the number of detection points, conduct statistical regression analysis to obtain the main influencing factor, and obtain the precision lookup table of the final block adjustment result of the remote sensing image based on the proportion of the survey area of the obtained main influencing factor.

[0115] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0116] It is possible to obtain the surface cover type data and terrain elevation data in the survey area of the block adjustment of the remote sensing image; then, according to the surface cover type data and terrain elevation data, calculate the area and proportion of each surface cover type on each terrain type in the survey area of the block adjustment respectively; then, according to the surface cover type data and terrain elevation data, collect enough and evenly distributed detection points for calculating the precision of the block adjustment result of the remote sensing image within the survey area; then design the sequence of the number and position of the sample detection points corresponding to each surface cover type on each terrain type, and calculate the mean square error sequence corresponding to each number of sample detection points; then, based on the mean square error sequence of the sample detection points corresponding to each surface cover type on each terrain type, statistically analyze the relationship between the block adjustment result precision, the terrain and land type area and proportion, and the number and distribution of detection points, and obtain the minimum number of inspection points required for checking the block adjustment result precision through marginal effect, and construct a precision lookup table for the block adjustment result of the remote sensing image; based on this, the relationship between the block adjustment result precision of the remote sensing image and the point position, surface cover, terrain conditions and the number of points can be established by using the method of constructing the precision lookup table for the block adjustment result of the remote sensing image, avoiding the uncertainty of the block adjustment result detection caused by the difference in the number of selected points and the uneven position distribution.

[0117] In some embodiments, the above surface cover type data includes cultivated land, forest land, grassland, shrub land, wetland, water body, tundra, artificial surface, bare land, glacier and permanent snow; the above terrain elevation data includes terrain categories, and the terrain categories include high mountains, mountains, hills and flatlands.

[0118] It should be noted that the surface cover types include, but are not limited to, cultivated land, forest land, grassland, shrub land, wetland, water body, tundra, artificial surface, bare land, glacier, and permanent snow cover, and may also include other surface cover types. The terrain categories include, but are not limited to, high mountains, mountains, hills, and flatlands, and may also include other terrain categories.

[0119] In some embodiments, based on the accuracy lookup table of the block adjustment results of the remote sensing image area network that has been constructed, the user can, according to actual needs, determine the detection point positions that are typical and representative and meet the requirements of the block adjustment accuracy inspection.

[0120] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0121] The above is the introduction of the method embodiments. The following further illustrates the solution of the present disclosure through device embodiments.

[0122] Figure 2 The block diagram of a device 200 for constructing an accuracy lookup table of the block adjustment results of a remote sensing image area network according to an embodiment of the present disclosure is shown. As Figure 2 shown, the device 200 includes:

[0123] An acquisition module 210, configured to acquire surface cover type data and terrain elevation data in the survey area of the block adjustment of the remote sensing image area network;

[0124] A processing module 220, configured to calculate the area and proportion of each surface cover type on each terrain type in the survey area of the block adjustment according to the surface cover type data and the terrain elevation data;

[0125] The processing module 220 is further configured to collect, according to the surface cover type data and the terrain elevation data, detection points for calculating the accuracy of the block adjustment results of the remote sensing image area network with a sufficient quantity and evenly distributed in the survey area;

[0126] The processing module 220 is further configured to design a sequence of the number and positions of the sample detection points corresponding to each surface cover type on each terrain type, and calculate the sequence of the mean square error corresponding to each number of sample detection points;

[0127] A generation module 230 is configured to, based on error sequences in sample detection points corresponding to various surface coverage types on various terrain types, statistically analyze the relationship between the adjustment result accuracy, the terrain type area and proportion, as well as the number and distribution of detection points, obtain the minimum number of checkpoints required for checking the accuracy of regional network adjustment results through marginal effects, and construct a lookup table for the accuracy of regional network adjustment results of remote sensing images.

[0128] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0129] In the technical solution of the present disclosure, the acquisition, storage, and application of user personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0130] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0131] Figure 3 A block diagram of an exemplary electronic device 300 capable of implementing the embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0132] The electronic device 300 includes a computing unit 301, which can execute various appropriate actions and processes according to a computer program stored in the ROM 302 or a computer program loaded from the storage unit 308 into the RAM 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The I / O interface 305 is also connected to the bus 304.

[0133] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disc, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0134] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308.

[0135] In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above may be executed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute method 100 in any other suitable manner (e.g., by means of firmware).

[0136] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general programmable processor, that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0139] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0140] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0141] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0142] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0143] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for constructing an accuracy lookup table for regional network adjustment results of remote sensing images, characterized in that, Including: Obtaining surface cover type data and terrain elevation data within the survey area for block adjustment of remote sensing images; Respectively calculating the area and proportion of each surface cover type on each terrain type within the survey area for block adjustment of remote sensing images based on the surface cover type data and the terrain elevation data; Collecting, within the survey area, a sufficient number of evenly distributed detection points for calculating the accuracy of the results of block adjustment of remote sensing images according to the surface cover type data and the terrain elevation data; Designing a sequence of the number and positions of sample detection points corresponding to each surface cover type on each terrain type, and calculating the sequence of mean square errors corresponding to each number of sample detection points; Based on the sequence of mean square errors of sample detection points corresponding to each surface cover type on each terrain type, statistically analyzing the relationship between the accuracy of adjustment results, the area and proportion of terrain types and surface cover types, as well as the number and distribution of detection points, obtaining the minimum number of inspection points required for checking the accuracy of block adjustment results through marginal effect, and constructing a lookup table for the accuracy of block adjustment results of remote sensing images; The step of, based on the sequence of mean square errors of sample detection points corresponding to each surface cover type on each terrain type, statistically analyzing the relationship between the accuracy of adjustment results, the area and proportion of terrain types and surface cover types, as well as the number and distribution of detection points, obtaining the minimum number of inspection points required for checking the accuracy of block adjustment results through marginal effect, and constructing a lookup table for the accuracy of block adjustment results of remote sensing images includes: using the marginal effect of the mean square error of detection points to perform linear fitting on the sequence of mean square errors of sample detection points corresponding to each surface cover type on each terrain type with the number of sample detection points as the horizontal axis and the mean square error as the vertical axis, and calculating the stationary points; wherein, the sequence of mean square errors of sample detection points corresponding to each surface cover type on each terrain type includes the sequence of mean square errors of a single surface cover type corresponding to a single terrain type or the sequence of mean square errors of a single terrain type corresponding to a single surface cover type; calculating the number of sample detection points corresponding to each stationary point according to the stationary points corresponding to each surface cover type on each terrain type to obtain the minimum number of inspection points; determining the influence weights of the terrain type and the surface cover type according to the number of sample detection points corresponding to each stationary point; and constructing a lookup table for the accuracy of block adjustment results of remote sensing images according to the influence weights, the area and proportion of each terrain type and surface cover type within the survey area for block adjustment of remote sensing images, as well as the number and distribution of the detection points to obtain the corresponding minimum number of detection points.

2. The method according to claim 1, characterized in that The step of designing a sequence of the number and positions of sample detection points corresponding to each surface cover type on each terrain type, and calculating the sequence of mean square errors corresponding to each number of sample detection points includes: Based on a preset detection point distribution algorithm, determining the number and positions of sample detection points on each surface cover type of each terrain type according to the area and proportion of each surface cover type of each terrain type within the survey area for block adjustment, as well as the number and distribution of the detection points; According to the quantity and location of sample detection points on each surface cover type for each terrain type, calculate the mean error sequences of the sample detection points corresponding to each surface cover type for each terrain type. The mean error sequences of the sample detection points corresponding to each surface cover type for each terrain type record the mean error information calculated under different strategies in the form of a list.

3. The method according to claim 1, wherein The obtaining of the surface cover type data and terrain elevation data within the survey area of the block adjustment of remote sensing images includes: According to the resolution and accuracy of the adjustment results within the survey area of the block adjustment of remote sensing images, obtain the initial surface cover type data and initial terrain elevation data within the survey area; Sort the initial surface cover type data and initial terrain elevation data in different scale orders according to the type, resolution, and accuracy of the obtained data; Rename the sorted initial surface cover type data and initial terrain elevation data according to preset rules, and store them in the database according to the pixel position to complete the construction of the geographical condition background database for block adjustment; Obtain the surface cover type data and terrain elevation data within the survey area of the block adjustment of remote sensing images from the geographical condition background database for block adjustment.

4. The method according to claim 1, wherein The calculating of the area and proportion of each surface cover type on each terrain type within the survey area of the block adjustment of remote sensing images according to the surface cover type data and the terrain elevation data includes: Perform normalization processing on the surface cover type data and the terrain elevation data according to a preset resolution. The normalization processing is to construct a complete pyramid data body of the geographical condition background database for block adjustment with multi-level continuity of resolution and accuracy; According to the normalized surface cover type data and terrain elevation data, calculate the area and proportion of each surface cover type on each terrain type within the survey area of the block adjustment of remote sensing images respectively.

5. The method according to claim 1, wherein The collecting of the detection points for calculating the accuracy of the block adjustment results of remote sensing images with sufficient quantity and uniform distribution within the survey area according to the surface cover type data and the terrain elevation data includes: Based on a preset detection point extraction model, collect the quantity and distribution of the detection points for determining the accuracy of the block adjustment results of remote sensing images. The sample detection points are reliable and sufficient in quantity.

6. The method according to any one of claims 1 to 5, characterized in that, The surface cover type data includes cultivated land, forest land, grassland, shrub land, wetland, water body, tundra, artificial surface, bare land, glacier, and permanent snow. The terrain elevation data includes terrain categories, and the terrain categories include high mountains, mountains, hills, and flatlands.

7. An apparatus for constructing an accuracy look-up table of an aerial triangulation result of remote sensing images, characterized in that, Includes: An obtaining module, used to obtain the surface cover type data and terrain elevation data within the survey area of the block adjustment of remote sensing images; A processing module, used to calculate the area and proportion of each surface cover type on each terrain type within the survey area of the block adjustment of remote sensing images respectively according to the surface cover type data and the terrain elevation data; The processing module is also used to collect the detection points for calculating the accuracy of the block adjustment results of remote sensing images with sufficient quantity and uniform distribution within the survey area according to the surface cover type data and the terrain elevation data; The processing module is further configured to design a sequence of the number and positions of sample detection points corresponding to each surface coverage type on each terrain type, and calculate a sequence of mean square errors corresponding to the sample detection points for each quantity. The generation module is configured to, based on the sequence of mean square errors of the sample detection points corresponding to each surface coverage type on each terrain type, statistically analyze the relationship between the accuracy of the adjustment result, the area and proportion of the terrain and land type, as well as the number and distribution of the detection points, obtain the minimum number of check points required for the accuracy inspection of the block adjustment result through the marginal effect, and construct a look-up table for the accuracy of the block adjustment result of the remote sensing image; specifically, the generation module is configured to use the marginal effect of the mean square error of the detection points to linearly fit the sequence of mean square errors of the sample detection points corresponding to each surface coverage type on each terrain type with the number of sample detection points as the horizontal axis and the mean square error as the vertical axis, and calculate the stationary points; wherein, the sequence of mean square errors of the sample detection points corresponding to each surface coverage type on each terrain type includes the sequence of mean square errors of a single surface coverage type corresponding to a single terrain type or the sequence of mean square errors of a single terrain type corresponding to a single surface coverage type; according to the stationary points corresponding to each surface coverage type on each terrain type, calculate the number of sample detection points corresponding to each stationary point to obtain the minimum number of check points; determine the influence weights of the terrain type and the surface coverage type according to the number of sample detection points corresponding to each stationary point; construct a look-up table for the accuracy of the block adjustment result of the remote sensing image according to the influence weights, the area and proportion in the survey area of the block adjustment of the remote sensing image of each surface coverage type on each terrain type, and the number and distribution of the detection points.

8. An electronic device, characterized in that, Comprising: At least one processor; And A memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to execute the method according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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