Remote sensing surveying and mapping data processing system

Abstract

The invention relates to the technical field of image data processing, in particular to a remote sensing surveying and mapping data processing system. The system at least comprises a first image acquisition module used for acquiring a remote sensing image; determining GCPs based on the remote sensing image; the GCPs measurement module is used for acquiring measurement data when the GCPs are measured; the second image acquisition module is used for acquiring a low-altitude image; the second image correction module is used for correcting the low-altitude image by using the GCPs and the measurement data corresponding to the GCPs so as to obtain a corrected low-altitude image; the correction point acquisition module is used for determining a correction point based on the corrected low-altitude image; and the first image correction module is used for correcting the remote sensing image by using the corrected low-altitude image and the correction points on the corrected low-altitude image so as to obtain a corrected remote sensing image. According to the invention, through cooperative processing of the two images, transition is carried out by means of the clear low-altitude image in the low-altitude state, and the remote sensing image is corrected in cooperation with the GCPs, so that a more accurate remote sensing image is obtained.

Description

Technical Field

[0001] This invention relates to the field of image data processing technology, and more specifically, to an image data processing system for remote sensing mapping. Background Technology

[0002] Remote sensing imagery involves acquiring a series of survey images using remote sensing technology. During the acquisition process, challenges arise due to factors such as long acquisition distances, weather conditions, and equipment limitations. However, existing technologies have effectively addressed these issues. For example:

[0003] Chinese Patent Publication No. CN115984134A discloses a method for intelligent enhancement of remote sensing images. The method includes: acquiring a remote sensing image and acquiring a preliminary enhanced image of the remote sensing image; acquiring a first region and a second region in the preliminary enhanced image; acquiring an enhancement factor for the first enhanced region; enhancing the first region to obtain a final enhanced first region; enhancing the wavelet coefficient amplitude of the second region; performing inverse wavelet transform on the enhanced wavelet coefficient amplitude to obtain a final enhanced second region; and obtaining a final enhanced image based on the final enhanced first and second regions. This invention achieves adaptive enhancement of regions with different texture information, thereby improving the contrast and detail clarity of the image.

[0004] Alternatively, Chinese Patent Publication No. CN115147313B discloses a geometric correction method, apparatus, device, and medium for elliptical orbit remote sensing images, relating to the field of remote sensing image processing technology. This method addresses the problem of high-precision geometric positioning of elliptical orbit remote sensing images. The method includes: interpolating satellite attitude data based on the elliptical orbit's operating mode; fitting and interpolating the elliptical orbit's orbit data; calculating a one-to-one correspondence between image-side coordinates and spatial coordinates based on the interpolated attitude data, the fitted and interpolated orbit data, and the elliptical orbit satellite's altitude; calculating the pixel resolution that normalizes different geographical locations to the same ground surface based on this one-to-one correspondence; resampling the elliptical orbit remote sensing image according to the pixel resolution parameters; obtaining the radiance value of each pixel after resampling; and achieving geometric correction of the elliptical orbit remote sensing image.

[0005] Most of the above patents or existing technologies only perform correction processing on a single final remote sensing image. However, this limits the number of reference images and makes it difficult to achieve the desired effect. This is especially true for high-altitude remote sensing images, where the image clarity is insufficient, making it difficult to clearly identify GCPs in the image. Furthermore, the number of GCPs that can be established on the ground is limited. Summary of the Invention

[0006] The purpose of this invention is to provide a remote sensing mapping data processing system to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, a remote sensing mapping data processing system is provided, comprising at least:

[0008] The first image acquisition module is used to acquire remote sensing images;

[0009] Ground control points (GCPs) are determined based on the remote sensing images;

[0010] The GCPs measurement module is used to acquire measurement data when measuring GCPs.

[0011] The second image acquisition module is used to acquire low-altitude images;

[0012] The second image correction module uses the GCPs and their corresponding measurement data to correct the low-altitude image to obtain the corrected low-altitude image.

[0013] The correction point acquisition module determines the correction point based on the corrected low-altitude image.

[0014] And, a first image correction module, which uses the corrected low-altitude image and correction points on the corrected low-altitude image to correct the remote sensing image, so as to obtain the corrected remote sensing image;

[0015] The first image acquisition module output is connected to the GCPs measurement module, the second image correction module input is connected to both the second image acquisition module and the GCPs measurement module, the second image correction module output is connected to the correction point acquisition module, and the first image correction module input is connected to both the second image correction module and the correction point acquisition module.

[0016] As a further improvement to this technical solution, the remote sensing images are acquired based on satellite-borne equipment.

[0017] As a further improvement to this technical solution, the remote sensing images are acquired based on equipment mounted on a UAV.

[0018] As a further improvement to this technical solution, the remote sensing images acquired by the first image acquisition module include radiometric data, atmospheric data, geometric data, and image data.

[0019] As a further improvement to this technical solution, the acquisition altitude of the low-altitude image is lower than that of the remote sensing image.

[0020] As a further improvement to this technical solution, the method for determining GCPs based on the remote sensing image includes the following steps:

[0021] S1.1 Divide the remote sensing image into several matrices to obtain several remote sensing image blocks;

[0022] S1.2 Automatically detect corner points on the periphery of remote sensing image blocks;

[0023] S1.3, perform redundancy processing on corner points and determine the redundant corner points as GCPs.

[0024] As a further improvement to this technical solution, the GCPs measurement module acquires GCPs measurement data including coordinate data, position within the remote sensing image, error information, and attribute information.

[0025] As a further improvement to this technical solution, the second image acquisition module acquires low-altitude images within a matrix containing GCPs.

[0026] As a further improvement to this technical solution, the first image correction module is preset with an ideal resolution between the resolution of the remote sensing image patch and the resolution of the low-altitude image, and the resolution of the remote sensing image patch containing GCPs and the resolution of the low-altitude image are made equal to the ideal resolution through scaling processing.

[0027] As a further improvement to this technical solution, the first image correction module performs the following steps for correcting the remote sensing image:

[0028] S2.1 Classify remote sensing image patches according to the different processing algorithms used;

[0029] S2.2 Based on the classification, multiple processing algorithms are performed simultaneously to correct remote sensing image patches containing GCPs, resulting in corrected remote sensing image patches.

[0030] S2.3. Restore the resolution of the corrected remote sensing image patch;

[0031] S2.4. Based on the restored and corrected remote sensing image blocks, correct the remote sensing image blocks that do not contain GCPs to obtain the corrected remote sensing image.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] 1. In this remote sensing and mapping data processing system, two images are processed together: one is a low-altitude image acquired at low altitude, and the other is a remote sensing image. The low-altitude image is corrected by ground-based ground-based ground-based calibration points (GCPs). Moreover, the low-altitude image is clearer, so it is used as a transition point. Specifically, calibration points are constructed on the low-altitude image to correct the remote sensing image in conjunction with the GCPs, so as to obtain the final remote sensing image.

[0034] 2. In this remote sensing mapping data processing system, the number of points for correcting remote sensing images is increased by using clearer low-altitude images. In this way, even if the number of GCPs is insufficient, the accuracy of correction can be improved by using correction points, and the cost of establishing GCPs is also reduced.

[0035] 3. In this remote sensing and mapping data processing system, by determining the corner points of the remote sensing image blocks, it is possible to ensure that the GCPs are evenly distributed across the entire remote sensing image. At the same time, based on the GCPs determined on the periphery, the GCPs at the edges of two image blocks can be quickly captured later, thus facilitating the integration of image blocks.

[0036] 4. In this remote sensing and mapping data processing system, the second image acquisition module acquires low-altitude images within a matrix containing GCPs. This reduces the number of low-altitude images acquired by the UAV, thereby alleviating memory burden and reducing the burden of UAV flight missions. Moreover, the acquired low-altitude images are more targeted.

[0037] 5. In this remote sensing and mapping data processing system, the remote sensing image block and the corresponding low-altitude image are at the same resolution through ideal resolution. In this way, the size and shape of the target area are at the same scale, which makes it easier to identify the target area during correction.

[0038] 6. In this remote sensing mapping data processing system, remote sensing image blocks are classified according to the different processing algorithms used, so that multiple processing algorithms can be performed simultaneously, which ensures processing accuracy and improves processing efficiency. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the structural composition of each module of the present invention;

[0040] Figure 2 This is a schematic diagram illustrating the remote sensing image correction principle of the present invention;

[0041] Figure 3 Flowchart of the method steps for determining GCPs from remote sensing images according to the present invention;

[0042] Figure 4 This is a schematic diagram of the image patch structure in the remote sensing image of the present invention;

[0043] Figure 5 This is a schematic flowchart illustrating the steps of the first image correction module in correcting remote sensing images in the third embodiment of the present invention.

[0044] The meanings of the labels in the diagram are as follows:

[0045] 100. First image acquisition module; 200. GCPs measurement module; 300. Second image acquisition module; 400. Second image correction module; 500. Correction point acquisition module; 600. First image correction module. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Remote sensing imagery involves acquiring a series of survey images using remote sensing technology. During the acquisition process, remote sensing imagery faces challenges such as long acquisition distances, weather conditions, and equipment limitations. Simply performing image correction processing alone is insufficient to achieve the desired results, especially for high-altitude remote sensing images, as the number of ground-based geophysical mapping (GCPs) is limited.

[0048] For this reason, see Figure 2 As shown, the present invention uses two images for processing. One is a low-altitude image 3 acquired in a low-altitude state, and the other is a remote sensing image. The low-altitude image 3 is corrected by ground-based GCPs (1). Moreover, the image acquired in a low-altitude state is clearer. Therefore, the low-altitude image 3 is used as a transition point. Specifically, correction points 5 are constructed on the low-altitude image 3 to correct the remote sensing image in conjunction with GCPs (1) to obtain the final remote sensing image.

[0049] In Example 1, please refer to Figure 1As shown, the present invention provides a remote sensing mapping data processing system, which includes a first image acquisition module 100, a GCPs measurement module 200, a second image acquisition module 300, a second image correction module 400, a correction point acquisition module 500, and a first image correction module 600; the first image acquisition module 100 is used to acquire remote sensing images (remote sensing images include radiometric data, atmospheric data, geometric data, and image data), and then determine the location on the ground where GCPs (1) need to be established based on the remote sensing images. Usually, multiple GCPs (1) are established, depending on the size of the remote sensing images and the actual situation on the ground. The output of the first image acquisition module 100 is then connected to the GCPs measurement module 200; the GCPs measurement module 200 is used to acquire measurement data 2 (specifically including coordinate data, position in the remote sensing image, and error information) when measuring GCPs (1). (and attribute information); the second image acquisition module 300 is used to acquire the low-altitude image 3; the input end of the second image correction module 400 is connected to the second image acquisition module 300 and the GCPs measurement module 200. The second image correction module 400 uses the GCPs (1) and the corresponding measurement data 2 to correct the low-altitude image 3 to obtain the corrected low-altitude image 4. The output end of the second image correction module 400 is then connected to the correction point acquisition module 500. The correction point acquisition module 500 determines the correction point 5 based on the corrected low-altitude image 4. Finally, the input end of the first image correction module 600 is connected to the second image correction module 400 and the correction point acquisition module 500. The first image correction module 600 uses the corrected low-altitude image 4 and the correction point 5 on the corrected low-altitude image 4 to correct the remote sensing image to obtain the corrected remote sensing image 6.

[0050] In this embodiment, the remote sensing image is acquired based on satellite-borne equipment, while the low-altitude image 4 is acquired by a drone. Therefore, it is undeniable that the acquisition altitude of the low-altitude image 4 is lower than that of the remote sensing image. In practice, the process of acquiring remote sensing images typically involves using sensors mounted on a satellite. The following is the specific process of acquiring remote sensing images:

[0051] First, select the sensor: Based on the type of information required for remote sensing mapping, such as optical, infrared, or radar, choose a suitable remote sensing sensor. After the satellite carrying these sensors enters its predetermined orbit, it will orbit the Earth according to a predetermined orbital period. The satellite's orbital design must ensure that it can periodically cover the geographic area to be mapped. When the satellite flies over the target area (i.e., the area to be mapped), the onboard sensors will capture electromagnetic wave information from the Earth's surface. This information is then converted into digital signals and stored in the satellite's data recording equipment. Next, the satellite's communication system will transmit the collected data back to the ground station, usually via radio waves. The time required can range from several minutes to several hours, depending on the distance between the satellite and the ground station and the bandwidth of the communication link. The data received by the ground station is usually raw, unprocessed data. This data needs to undergo preprocessing, including radiometric calibration, atmospheric correction, geometric correction, and cloud / fog removal, to eliminate the influence of sensor noise and atmospheric and cloud factors. The preprocessed remote sensing images can be further processed, such as image enhancement, classification, and interpretation, to facilitate user understanding and analysis. For example, supervised or unsupervised classification algorithms can be used to classify land use types, or change detection technology can be used to monitor surface changes.

[0052] The above process is illustrated with an example:

[0053] For example, for vegetation monitoring, the OLI (Operational Land Imager) or ETM+ (Enhanced Thematic Mapper Plus) sensors of the Landsat series satellites are used because they provide multiple spectral bands, including the red-edge and near-infrared bands for vegetation monitoring. Then, the vegetation areas to be mapped are determined, and the satellite's transit schedule is planned so that the first image acquisition module 100 can acquire remote sensing images during critical periods. When the satellite transits, the onboard sensors capture multispectral image data of the target area, which includes vegetation reflectance information, reflecting the health status and biomass of the vegetation. Next, the downloaded remote sensing images need to be radiometrically calibrated and atmospherically corrected to eliminate sensor calibration errors and the influence of the atmosphere on the signal. At the same time, geometric correction is performed to ensure that the images match the actual geographical location.

[0054] The key improvement in this case lies in the geometric correction of the remote sensing images, as detailed in [link to relevant documentation]. Figure 1 As shown, GCPs(1) are determined based on remote sensing images. Since the remote sensing images can display images of the target area, the GCPs(1) determined in the remote sensing images need to meet the following requirements:

[0055] The GCPs (1) are evenly distributed throughout the remote sensing image to ensure that the entire remote sensing image is well corrected and to avoid GCPs (1) being concentrated in a certain part of the remote sensing image, especially the edge region.

[0056] Points with obvious, unique features that are easy to identify on remote sensing images and ground maps are designated as GCPs (1), such as road intersections, buildings, large rocks, river bends, etc.

[0057] The location of GCPs(1) needs to be verifiable through field investigation or by using high-resolution maps and other geographic information system data;

[0058] Try not to select linear features such as straight lines and roads as GCPs (1), because they may become less obvious in remote sensing images due to projection distortion;

[0059] Based on the size of the remote sensing image and the expected correction accuracy, select a sufficient number of GCPs (1). Generally speaking, the larger the remote sensing image or the higher the required correction accuracy, the more GCPs (1) are needed.

[0060] When selecting GCPs(1), the quality of the remote sensing image should be considered, and areas that may affect feature recognition, such as occlusion, fog, and shadows, should be avoided.

[0061] Once GCPs(1) are determined, the following measurement data 2 is typically required when performing measurements on remote sensing images of vegetation using the GCPs measurement module 200:

[0062] The geographic coordinates of GCPs(1), including longitude and latitude, may also require elevation information in some cases, especially when stereo correction or topographic correction of remote sensing images is important.

[0063] On the remote sensing image, it is necessary to measure the pixel coordinates (row and column numbers) of the corresponding points of GCPs(1);

[0064] Detailed descriptions and identifiers of GCPs(1) are recorded to accurately identify these points on the image;

[0065] Record the time of data collection for GCPs(1), which is particularly useful for monitoring changes in vegetation over time;

[0066] Obtaining relevant metadata of remote sensing images, including sensor type, imaging date, solar elevation angle, observation angle, etc., helps to better understand the performance of GCPs(1) on remote sensing images.

[0067] It should be noted that the measurement work of GCPs(1) usually requires the use of high-precision Global Positioning System (GPS) equipment or other accurate Geographic Information System (GIS) tools.

[0068] Then, the second image acquisition module 300 acquires low-altitude image 3 through a low-altitude flying UAV. The acquired low-altitude image 3 is clearer than the remote sensing image, and the UAV can adjust its flight altitude and path as needed to adapt to complex terrain and changing weather conditions. It can also acquire images of the required area in real time, which is suitable for emergency response. It can be seen that the result obtained by correcting the low-altitude image 3 using GCPs (1) is more accurate than that obtained by remote sensing image and low-altitude image 3. Therefore, the second image correction module 400 selects a suitable geometric correction model according to the data and requirements. Commonly used models include affine transformation, polynomial transformation and projective transformation. Transformation), for the selected correction model, establish a mathematical model to describe the relationship between the coordinates of GCPs(1) on the ground and the image. For example, for the affine transformation model, it can handle the linear and nonlinear errors in the image caused by shooting angle, sensor distortion and other reasons. The model assumes that there is a linear relationship between the image coordinate system and the ground coordinate system. Add a translation vector and use the least squares method or other optimization techniques to estimate the model parameters according to the coordinates of GCPs(1). The goal is to minimize the difference between the actual ground coordinates and the predicted coordinates of all GCPs(1). Once the model parameters are estimated, they can be applied to the entire low-altitude image 3 to perform geometric transformation on each pixel, thereby realizing the geometric correction of the low-altitude image 3.

[0069] Through the above process, a precise corrected low-altitude image 4 has been obtained. The correction point acquisition module 500 selects a correction point 5 with obvious features around GCPs (1) on the corrected low-altitude image 4 (the method for determining the correction point 5 is referred to GCPs (1). Moreover, since the corrected low-altitude image 4 has been corrected, the determined correction point 5 can be obtained with relatively accurate points without the need for measurement data acquisition). This complements the GCPs (1). Finally, the first image correction module 600 combines the corrected low-altitude image 4, the correction point 5, and GCPs (1) to correct the remote sensing image and obtain the corrected remote sensing image 6. The specific steps are as follows:

[0070] Registering the remote sensing image with the corrected low-altitude image 4 typically involves selecting some common feature points and using these points to determine the transformation relationship between the two images. Feature matching algorithms such as SIFT, SURF, or ORB can be used to identify these feature points.

[0071] Once the two images are registered, geometric transformations can be used to correct the remote sensing images. This usually involves mathematical models such as affine transformations, projective transformations, or polynomial transformations. The choice of which model to use depends on the characteristics of the images and the requirements of the correction.

[0072] Fine adjustment using correction points and GCPs (1): Based on the geometric transformation, fine adjustment can be made using correction points and GCPs. For each correction point 5 and GCPs (1), its position in the corrected remote sensing image can be calculated and compared with the actual position. Based on the comparison results, the geometric transformation parameters can be further adjusted to finally obtain a more accurate corrected remote sensing image 6.

[0073] Preferably, additional verification points can be selected in the corrected low-altitude image 4. Then, appropriate indicators and methods are needed to verify the corrected remote sensing image 6. Commonly used indicators include root mean square error (RMSE) and mean absolute error (MAE), which can help evaluate the accuracy and reliability of the correction.

[0074] After the remote sensing image preprocessing is completed and the corrected remote sensing image 6 is obtained, the Normalized Difference Vegetation Index (NDVI) and other algorithms are used to calculate the NDVI from the reflectance of the red and near-infrared bands. The NDVI value ranges from -1 to 1, with higher values ​​generally indicating healthier vegetation cover. Then, the remote sensing image is classified to distinguish different land cover types, such as forests, grasslands, and water bodies. This can be done through supervised or unsupervised classification methods, and the accuracy of classification can be improved by combining ground validation data. Depending on the specific application, remote sensing images acquired at different time points are compared to monitor the seasonal changes and long-term trends of vegetation. This helps to assess vegetation degradation, restoration, or other environmental changes, so that the monitoring results can be applied to fields such as environmental protection, agricultural management, urban planning, and climate change research. For example, by monitoring deforestation, carbon emissions can be assessed and corresponding policy measures can be formulated.

[0075] In the second embodiment, both the remote sensing image and the low-altitude image 4 are acquired based on the equipment carried by the UAV, and the acquisition altitude of the low-altitude image 4 is lower than that of the remote sensing image. Compared with the remote sensing image acquired by the satellite in the first embodiment, the remote sensing image acquired in this embodiment has a limited mapping range, but the clarity of the obtained remote sensing image is higher than that of the remote sensing image acquired by the satellite. However, the determination of GCPs (1) and the correction of the remote sensing image are not improved in this embodiment, so they will not be described in detail here.

[0076] In the third embodiment, it can be applied to remote sensing images acquired based on satellite-borne equipment or remote sensing images acquired based on UAV-borne equipment, specifically the determination of GCPs(1) and improvements regarding remote sensing image correction, see [link to relevant documentation]. Figure 3As shown, the method steps for determining GCPs(1) based on the remote sensing image are as follows:

[0077] S1.1 Divide the remote sensing image into several matrices to obtain several remote sensing image patches. The following is a specific algorithm step for dividing the remote sensing image into several image patches:

[0078] Reading remote sensing images: First, use the corresponding image processing libraries such as OpenCV and GDAL to read the remote sensing images to be processed.

[0079] import Image from PIL

[0080] #Read image

[0081] image=Image.open('path_to_your_image.tif')

[0082] Set block size: Set the block size according to the required block size (width and height);

[0083] block_width = 256 # Sets the width of the block

[0084] block_height = 256 # Sets the height of the block

[0085] Image block partitioning: By iterating through the image, the image is divided into blocks of a specified size, and each block is stored as a new matrix;

[0086] # Calculate the number of rows and columns

[0087] rows, cols=image.size[1] / / block_height, image.size[0] / / block_width

[0088] # Initialize a list to store all image blocks

[0089] image_blocks = []

[0090] # Traverse the image and generate blocks

[0091] for iin range(rows):

[0092] forj in range(cols):

[0093] #Starting coordinates of the computation block

[0094] start_row = i * block_height

[0095] start_col = j * block_width

[0096] #Extract block

[0097] block=image.crop((start_col, start_row, start_col+block_width, start_row+block_height))

[0098] #Add to block list

[0099] image_blocks.append(block)

[0100] Save or process image patches: Save each image patch to the file system or perform further processing on it, such as feature extraction, classification, etc.

[0101] #Save image blocks to file system

[0102] for idx, block in enumerate(image_blocks):

[0103] block.save(f'block_{idx}.tif')

[0104] Clean up resources: After completing the operation, release the image objects in memory;

[0105] del image

[0106] The code examples above are written in Python, assuming the Pillow library is used to read and process images. In practical applications, it may be necessary to select appropriate libraries and functions based on the specific remote sensing image format and processing requirements. In addition, if the image size is not an integer multiple of the block size, the last row or the last column may need to be special. However, the division of image blocks is based on existing technical means, which will not be exhaustively listed here.

[0107] S1.2 Automatic detection of corner points on the periphery of remote sensing image blocks. Using image processing techniques, corner points in remote sensing images are automatically detected. These corner points are often stable feature points and are suitable as GCPs (1). For example, in remote sensing images with simple terrain such as mountains or deserts, corner points at the terrain undulations on the periphery of the remote sensing image are automatically detected as GCPs (1). Figure 4As shown, the area outside the dashed box is the outer region of the image patch. GCPs (1) are determined within the outer region. This ensures that GCPs (1) are evenly distributed across the entire remote sensing image. Furthermore, based on the GCPs (1) determined from the outer region, the GCPs (1) at the edges of two image patches can be quickly captured later, thus facilitating the integration of the image patches.

[0108] S1.3. For corner redundancy processing, the clustering method is used to select representative GCPs (1) in each image block, or the clustering algorithm is used to identify typical image blocks in the remote sensing image and select GCPs (1) from them. That is, the remaining corner points after redundancy are determined as GCPs (1).

[0109] Preferably, the second image acquisition module 300 acquires low-altitude images 3 within a matrix containing GCPs (1), which can reduce the number of low-altitude images 3 acquired by the UAV, thereby reducing the memory burden and the burden of UAV flight missions, and the acquired low-altitude images are more targeted.

[0110] In addition, the first image correction module 600 presets an ideal resolution between the resolution of the remote sensing image block and the resolution of the low-altitude image 3, and makes the resolution of the remote sensing image block containing GCPs (1) and the resolution of the low-altitude image 3 equal to the ideal resolution through scaling processing.

[0111] In addition, see Figure 5 As shown, the steps of the first image correction module 600 in correcting the remote sensing image are as follows:

[0112] S2.1. Classify remote sensing image patches according to the different processing algorithms used. As can be seen from the first embodiment, select an appropriate geometric correction model according to the data and requirements. Commonly used models include affine transformation, polynomial transformation and projective transformation.

[0113] S2.2 According to the classification, multiple processing algorithms are carried out simultaneously. Specifically, the remote sensing image block containing GCPs(1) is corrected based on the corrected low-altitude image 4, the correction point 5 on the corrected low-altitude image 4 and GCPs(1) to obtain the corrected remote sensing image block.

[0114] S2.3. Restore the resolution of the corrected remote sensing image patch;

[0115] S2.4. Based on the restored and corrected remote sensing image blocks, the remote sensing image blocks that do not contain GCPs (1) are corrected to obtain the corrected remote sensing image 6.

[0116] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A remote sensing mapping data processing system, characterized in that, At least including: The first image acquisition module (100) is used to acquire remote sensing images; GCPs (1) are determined based on the remote sensing images; GCPs measurement module (200), which is used to acquire measurement data (2) when measuring GCPs (1); The second image acquisition module (300) is used to acquire low-altitude images (3); The second image correction module (400) uses the GCPs (1) and the corresponding measurement data (2) to correct the low-altitude image (3) to obtain the corrected low-altitude image (4). The correction point acquisition module (500) determines the correction point (5) based on the corrected low-altitude image (4); And, a first image correction module (600) uses the corrected low-altitude image (4) and the correction points (5) on the corrected low-altitude image (4) to correct the remote sensing image to obtain the corrected remote sensing image (6). The output of the first image acquisition module (100) is connected to the GCPs measurement module (200), the input of the second image correction module (400) is connected to the second image acquisition module (300) and the GCPs measurement module (200), the output of the second image correction module (400) is connected to the correction point acquisition module (500), and the input of the first image correction module (600) is connected to the second image correction module (400) and the correction point acquisition module (500).

2. The remote sensing mapping data processing system according to claim 1, characterized in that, The remote sensing images were acquired using satellite-borne equipment.

3. The remote sensing mapping data processing system according to claim 1, characterized in that, The remote sensing images were acquired using equipment mounted on a drone.

4. The remote sensing mapping data processing system according to any one of claims 1-3, characterized in that, The remote sensing images acquired by the first image acquisition module (100) include radiometric data, atmospheric data, geometric data, and image data.

5. The remote sensing mapping data processing system according to any one of claims 1-3, characterized in that, The low-altitude image (4) is acquired at a lower altitude than the remote sensing image.

6. The remote sensing mapping data processing system according to any one of claims 1-3, characterized in that, The method steps for determining GCPs(1) based on the remote sensing image are as follows: S1.1 Divide the remote sensing image into several matrices to obtain several remote sensing image blocks; S1.2 Automatically detect corner points on the periphery of remote sensing image blocks; S1.3, perform redundancy processing on corner points and determine the redundant corner points as GCPs (1).

7. The remote sensing mapping data processing system according to claim 1, characterized in that, The GCPs measurement module (200) acquires the measurement data (2) of GCPs (1), including coordinate data, position within the remote sensing image, error information, and attribute information.

8. The remote sensing mapping data processing system according to claim 6, characterized in that, The second image acquisition module (300) acquires low-altitude images (3) within a matrix containing GCPs (1).

9. The remote sensing mapping data processing system according to claim 8, characterized in that, The first image correction module (600) presets an ideal resolution between the resolution of the remote sensing image block and the resolution of the low-altitude image (3), and makes the resolution of the remote sensing image block containing GCPs (1) and the resolution of the low-altitude image (3) equal to the ideal resolution through scaling processing.

10. The remote sensing mapping data processing system according to claim 9, characterized in that, The steps of the first image correction module (600) in correcting the remote sensing image are as follows: S2.1 Classify remote sensing image patches according to the different processing algorithms used; S2.2 According to the classification, multiple processing algorithms are performed simultaneously to correct the remote sensing image blocks containing GCPs(1) and obtain the corrected remote sensing image blocks. S2.

3. Restore the resolution of the corrected remote sensing image patch; S2.

4. Based on the restored and corrected remote sensing image blocks, the remote sensing image blocks that do not contain GCPs (1) are corrected to obtain the corrected remote sensing image (6).

Images