Automatic cutting and correcting airborne hyperspectral data processing method and system

Abstract

The invention discloses an airborne hyperspectral data processing method and system capable of automatic cutting and correction, and the automatic track cutting technology achieves the effective air strip extraction and cutting through the algorithm analysis of GPS track data, and the combination of the longitude and latitude, course, altitude and other information, on one hand, can remove redundant spectral data, greatly reduces the subsequent calculation amount, and improves the accuracy of data processing. The data processing speed is obviously improved, and the problem of low efficiency of traditional manual cutting is solved; and on the other hand, effective air strips are accurately extracted, effective data omission or redundant residues caused by manual operation are avoided, high-quality basic data are provided for subsequent correction processing, and the accuracy of overall data processing is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of spectral processing, and in particular to an automatic clipping and correction airborne hyperspectral data processing method and system. Background Art

[0002] Airborne hyperspectral data, with its high resolution and multi-band characteristics, plays an irreplaceable role in water quality monitoring, ecological assessment, mineral exploration, and other fields, providing key technical support for accurately acquiring surface and water body information. However, problems in the current processing of airborne hyperspectral data, such as data clipping and geometric correction, seriously restrict the full utilization of its application effectiveness.

[0003] Traditional methods for processing flight path data rely on manual route clipping of airborne hyperspectral data. This is not only inefficient but also subject to operator experience differences, making it prone to incomplete extraction of valid flight path data or excessive redundant data. Redundant data directly increases the computational load of subsequent geometric corrections, slowing overall processing speed and even causing deviations in the correction baseline due to interference from invalid information. The omission of valid data also compromises the integrity of the final analysis results, making it difficult to meet the stringent data accuracy requirements of scenarios such as water quality monitoring.

[0004] Furthermore, when it comes to geometric correction of airborne hyperspectral data, line-scan hyperspectral equipment relies excessively on high-precision IMU modules. While high-precision IMUs can guarantee correction accuracy to a certain extent, the high hardware cost significantly increases the deployment and maintenance costs of airborne systems, making them unaffordable for small- to medium-scale applications or cost-sensitive scenarios, limiting the widespread adoption of airborne hyperspectral technology. These combined challenges not only affect the accuracy and efficiency of airborne hyperspectral data applications in areas such as water quality monitoring, but also hinder the widespread adoption of this technology due to cost barriers. These issues represent a core bottleneck that urgently needs to be overcome in airborne hyperspectral data processing.

[0005] In summary, an automatic cropping and correction airborne hyperspectral data processing method and system is needed to address the shortcomings of the existing technology. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides an airborne hyperspectral data processing method and system with automatic clipping and correction. The method aims to solve the problems of low processing efficiency, high cost, and difficulty in ensuring data accuracy due to manual route data clipping, incomplete effective data extraction or excessive redundant data, and high hardware cost reliance on high-precision IMU modules in the correction process, which in turn affect the application effect.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for processing airborne hyperspectral data with automatic cropping and correction, comprising the following steps:

[0008] Step S1: Airborne multi-source data acquisition: Using a hyperspectral camera, visible light camera, GPS, and zenithal light equipment, data is collected according to the planned route. The heading angle is deduced based on the changes in longitude and latitude at adjacent moments. The zenithal light equipment monitors the changes in ambient light in real time, and the exposure time of the hyperspectral camera is adjusted in real time based on the collected data. The reflectivity is calculated in real time using the zenithal light data and hyperspectral data.

[0009] Step S2: Automatic flight strip cutting: using the trajectory clustering algorithm to identify the core route, screening the qualified track clusters as the target route segments, generating a complete operation flight strip and completing the cutting;

[0010] Step S3: constructing a georeferenced benchmark, preprocessing the visible light image, synthesizing an orthophoto map, and using GPS information to assign geographic coordinates to each pixel of the map to form a georeferenced map;

[0011] Step S4: Orthophoto hyperspectral data generation: divide the valid flight strip data into small blocks, perform preliminary matching based on GPS and orthophoto basemap, extract feature points and match them to obtain the transformation matrix, apply the transformation matrix to each wavelength dimension, and generate an orthophoto hyperspectral image with UTM coordinates.

[0012] Optionally, in step S1, data is collected according to the planned route in the following manner:

[0013] The hyperspectral camera, zenithal light, and GPS collect data at the same frequency. The GPS obtains the current timestamp and latitude and longitude information in real time. Each frame of the hyperspectral camera and zenithal light corresponds to a set of GPS data. Each visible light image obtains the corresponding GPS data through the relationship between the acquisition frequencies and the timestamp.

[0014] Optionally, the heading angle in step S1 is derived in the following manner:

[0015] Based on the temporal changes of longitude and latitude at adjacent moments, the heading angle of each frame of data is derived using the spherical azimuth formula and the result is converted into an angle value from 0° to 360°. The inherent parameters of the equipment are exported as data for automatic flight strip clipping and geometric correction.

[0016] Optionally, the core routes are identified in step S2 in the following manner:

[0017] The spatial neighborhood radius and the minimum number of points that are suitable for the spacing between operating routes are set. By calculating the spatial density distribution of trajectory points, spatially continuous clustered trajectory points with a number of points greater than or equal to the minimum number in the neighborhood are divided into the same track cluster. The three track clusters containing the longest continuous track points are selected, and the average heading angle of each cluster is calculated. Starting from the longest route, if there is a track cluster whose heading is ±180° opposite to that of any of the other two clusters, then this cluster is selected as the reference route.

[0018] Optionally, in step S2, the qualified track clusters are selected as target route segments in the following manner:

[0019] Taking the determined reference route as a reference, traverse all remaining track clusters, filter out the track clusters whose length exceeds the set threshold and whose heading angle deviation is greater than the reference route, mark them as target route segments, and exclude track clusters whose deviation exceeds the limit or whose length is insufficient.

[0020] Optionally, step S2 further includes fine-grained screening of flight strips by:

[0021] The velocity parameters calculated based on the average elevation and longitude and latitude changes of each track cluster are used to eliminate discrete points. A sliding window is set by linear fitting to check the slope stability of the trajectory within the window and the distance between adjacent points. Points with abnormal slopes or distances exceeding a certain range of the average distance are removed from the target label, and continuous trajectory segments with stable motion states are retained.

[0022] Optionally, the complete operation strip is generated and cut in step S2 in the following manner:

[0023] Integrate all target label route segments that have passed the refined screening to generate a complete and effective operation strip. Based on business needs, cut out all track segments within the specified label range from the complete strip, filter out the cutting range of the effective strip, and complete automatic strip cutting.

[0024] Optionally, the formation of the georeferenced map in step S3 is carried out in the following manner:

[0025] Image distortion caused by lens optical distortion is eliminated based on the camera's own parameters. The brightness and contrast of the image are then adjusted under different lighting conditions. Cross-image matching is performed by comparing the similarity of feature points between different images. The geometric transformation relationship between the images is calculated based on the matching results. After aligning multiple images, the pixel grayscale values ​​in the overlapping areas are smoothly transitioned to form an orthophoto map.

[0026] By using the GPS positioning information associated with each image when it was taken, each pixel of the orthophoto basemap is assigned corresponding geographic coordinates to form a geographic reference basemap with a unified spatial benchmark.

[0027] Optionally, the orthophoto hyperspectral data in step S4 is generated by:

[0028] Step A1: Block processing of hyperspectral data. The effective flight zone hyperspectral data is divided into several data blocks according to a preset size. The GPS positioning information corresponding to the middle frame of each data block is extracted and converted to UTM coordinates. The image corresponding to each small block on the orthophoto basemap is cut out by combining the hyperspectral image and the geographic pixel size of the visible light orthophoto basemap. The approximate UTM coordinates corresponding to the four corners of the cropped hyperspectral block can be calculated based on the UTM coordinates of the center block and the geographic pixel size. The pixel position is found using the UTM coordinates, and a slightly larger area is cut out based on the geographic range of the data block.

[0029] Step A2: Image matching and transformation matrix acquisition: Select three channels from the segmented hyperspectral data to synthesize a visible light image, or select a channel with a high signal-to-noise ratio as the matching image. Extract feature points from the obtained visible light orthophoto image blocks corresponding to the geographic coordinates of the hyperspectral image blocks, perform feature point matching, and obtain and save the transformation matrix based on the matching relationship.

[0030] Step A3: Generate orthophoto hyperspectral images. Traverse all hyperspectral data blocks and apply the corresponding spatial transformation matrix to the two-dimensional data array of each wavelength dimension to generate a complete orthophoto hyperspectral image with UTM geographic coordinate attributes, thus completing the precise association between spectral data and geographic space.

[0031] An automatic clipping and correction airborne hyperspectral data processing system adopts the automatic clipping and correction airborne hyperspectral data processing method, characterized by comprising a data acquisition module, an automatic flight strip clipping module, a geographic reference benchmark construction module and an orthophoto hyperspectral data generation module;

[0032] The data acquisition module is responsible for collecting data using multi-source sensors such as hyperspectral cameras, visible light cameras, GPS, and zenithal light equipment according to the predetermined route;

[0033] The automatic flight strip cutting module is used to automatically identify core routes based on the trajectory clustering algorithm, select qualified track clusters as target route segments, and complete the refined screening and final cutting of flight strips;

[0034] The georeferenced benchmark construction module is used to pre-process the visible light image to eliminate optical distortion, adjust the brightness and contrast, and synthesize the orthophoto map. It also uses GPS information to assign geographic coordinates to each pixel of the map to form a unified spatial benchmark.

[0035] The ortho-hyperspectral data generation module is used to segment the acquired valid flight strip data into small blocks and perform preliminary matching between GPS and the ortho-map. It then extracts and matches feature points to generate transformation matrices. Finally, these matrices are applied to the data at each wavelength dimension to generate ortho-hyperspectral images with UTM coordinates.

[0036] Beneficial effects of the present invention:

[0037] 1. In this invention, the automatic track clipping technology extracts and clips effective flight strips by performing algorithmic analysis on GPS trajectory data and combining information such as latitude and longitude, heading, and altitude. On the one hand, it can remove redundant spectral data, significantly reduce the amount of subsequent calculations, significantly improve data processing speed, and solve the problem of low efficiency of traditional manual clipping. On the other hand, by accurately extracting effective flight strips, it avoids the omission of valid data or redundant residuals caused by manual operation, provides high-quality basic data for subsequent correction processing, and ensures the accuracy of overall data processing.

[0038] 2. This invention uses a spectral data correction technology based on GPS and orthophotos, eliminating the need for a high-precision IMU module. Instead, it uses a visible light camera to capture photos and process them into orthophotos. Projection conversion and registration are performed using GPS data, and geometric calibration is achieved using a transformation matrix. Compared to traditional calibration methods that rely on IMU modules, this significantly reduces hardware costs, making hyperspectral data processing solutions more economical and accessible. Furthermore, combined with the precise data automatically cropped in the early stages, the correction process can reduce redundant information interference, further improving the geometric accuracy and reliability of the spectral data.

[0039] 3. In this invention, the synergistic effect of automatic track clipping and GPS-orthophoto correction technology forms an efficient and complete processing flow. The high-quality data after automatic clipping provides more accurate input for the correction process, reducing the source of error in the correction process; and the low-cost advantage of the correction technology enables the processing solution to be applied in more scenarios. The combination of the two not only improves the efficiency and accuracy of data processing, but also lowers the application threshold, helping airborne hyperspectral data to play a greater role in water quality monitoring, ecological assessment and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The present invention is a flow chart of a method.

[0041] Figure 2 This is an automatic exposure adjustment diagram of the present invention.

[0042] Figure 3 A schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0043] In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] like Figure 1 and Figure 2 As shown in FIG, a method for processing airborne hyperspectral data with automatic cropping and correction includes the following specific contents:

[0045] Step 1: Airborne multi-source data acquisition.

[0046] The drone is equipped with a hyperspectral camera, visible light camera, GPS, and a zenithal light device, collecting data along a planned route. The hyperspectral camera, zenithal light, and GPS collect data at the same frequency. The GPS acquires the current timestamp and latitude and longitude information in real time. Each frame of the hyperspectral camera and zenithal light corresponds to a set of GPS data. The visible light camera collects data at a slower rate, but each visible light frame has corresponding GPS data. This data can be correlated with the other data through the relationship between the acquisition frequencies and timestamps.

[0047] Input map data, collect data according to the planned route through hyperspectral cameras, visible light cameras, GPS and zenithal light equipment, identify water body data, compare it with the map water body data, and correct the hyperspectral data.

[0048] Based on the temporal changes in longitude and latitude at adjacent moments, the spherical azimuth formula is used to derive the heading angle for each frame of data and convert the result into an angle value ranging from 0° to 360° (0° represents true north, and the angle increases clockwise). Furthermore, inherent device parameters (such as the hyperspectral sensor's field of view and the visible light camera's focal length) are also derived, providing foundational support for subsequent automatic flight strip cropping and geometric correction.

[0049] At the same time, if Figure 2As shown, the zenithal light sensor monitors ambient light changes in real time, and the control program automatically performs exposure based on the collected data to ensure the optimal signal-to-noise ratio for the zenithal light sensor data. The hyperspectral sensor also automatically performs exposure in real time through the control program, thereby obtaining spectral data with the optimal signal-to-noise ratio. Algorithmic analysis and processing of the acquired zenithal spectral data and hyperspectral data yields real-time reflectance hyperspectral data. This processing removes ambient light intensity variations caused by cloudiness and water vapor, resulting in more accurate reflectance data. The pixel digital quantization value (DN value) is linearly related to exposure time. Algorithmic analysis of the zenithal spectral data and hyperspectral data calculates reflectance in real time, removing ambient light intensity variations caused by cloudiness or water vapor, and obtaining accurate reflectance data. This enables adaptive exposure adjustment, completing real-time reflectance acquisition and ambient light correction.

[0050] In order to ensure data alignment, a synchronous trigger controller is also introduced into the system, so that the zenith spectrum sensor and the hyperspectral sensor collect data at the same time, thereby ensuring that the data collected by the two devices is at the same time when the ambient light changes.

[0051] The algorithm is calculated as follows:

[0052] First, the ratio data1 of the hyperspectral data and the current exposure time of the hyperspectral data is calculated, and the zenith light data is collected so that its wavelength is consistent with the hyperspectral data;

[0053] Secondly, the ratio of the zenithal light data to the zenithal light exposure time, data2, is calculated to eliminate the data fluctuation caused by the ambient light change;

[0054] Finally, calculate the ratio data3 of the ratio data1 and the ratio data2, and convert the spectral data into reflectance data through the calibration model k+b. The reflectance data is equal to the ratio data3 multiplied by the slope k of the calibration model plus the intercept b;

[0055] The calibration model can be derived through data collection and analysis in the laboratory. For example, we can derive the calibration model by collecting known reflectivity plates.

[0056] At the same time, the corrected hyperspectral data and the real-time calculated reflectivity are used as the basis for the selection and cutting of flight strips.

[0057] Step 2: Automatic strip cutting.

[0058] (1) Trajectory clustering and core flight path identification: Using density clustering algorithms such as DBSCAN, set the spatial neighborhood radius (e.g., 50m) and the minimum number of points (e.g., 10) that are suitable for the spacing between operating routes. By calculating the spatial density distribution of trajectory points, spatially continuous clustered trajectory points with a number of points greater than or equal to the minimum number of points in the neighborhood are divided into the same trajectory cluster, and isolated points with a density lower than the threshold are excluded. On this basis, three trajectory clusters containing the longest continuous trajectory points are selected, and the average heading angle of each cluster is calculated. Starting from the longest route, if there is a trajectory cluster whose heading is ±180° opposite to that of any of the other two clusters, then this cluster is selected as the reference route to provide a reference standard for subsequent flight path matching. This is because the longest continuous route may be from the take-off point to the collection starting point, especially when collecting seawater.

[0059] (2) Track matching and marking: Using the established baseline route as a reference, we traverse all remaining track clusters and select those whose length exceeds 30% of the baseline route and whose heading angle deviation is ≤10° (the deviation range includes reverse deviations of ±180° from the baseline heading). These eligible track clusters are uniformly marked as target route segments. At the same time, track clusters with excessive deviations or insufficient lengths are excluded to ensure that the retained track clusters match the baseline route in terms of spatial scale and heading consistency, laying the foundation for subsequent refined screening.

[0060] (3) Fine-tuning of flight paths: First, the speed parameters calculated based on the average elevation and longitude and latitude changes of each track cluster are combined to eliminate discrete points that have not entered the formal collection phase to reduce the interference of non-operational tracks on the results. Then, a sliding window (e.g., 10 points) is set through linear fitting to check the slope stability of the track within the window and the distance between adjacent points. Points with abnormal slopes or distances exceeding a certain range of the average distance are removed from the target label. Finally, continuous track segments with stable motion are retained to improve the accuracy and reliability of the flight paths. Generally, the points that are screened out are unstable points at both ends.

[0061] (4) Strip Extraction and Clipping: Integrate all target label segments that have passed the refined screening to generate a complete and effective operational strip. Based on business needs, all track segments within the specified label range are clipped from the complete strip, and the effective strip clipping range is determined, completing the entire process of automatic strip clipping.

[0062] Step 3: Georeferenced datum construction (orthophoto basemap generation). Use visible light imagery as orthophoto basemap. Figure 1One reason is that visible light orthophotos don't require IMU data, and another is that hyperspectral cameras use line scans, which may result in missed scans. The image sequences captured by the airborne visible light camera are preprocessed. Image distortion caused by lens optical distortion is first eliminated based on the camera's parameters (such as focal length and principal point position). Brightness and contrast are then adjusted under varying lighting conditions to maintain a consistent grayscale distribution across all images. Feature points are then extracted from each image (these feature points remain stable under scale variations). Cross-image matching is performed by comparing the similarity of feature points between different images, while incorrectly matched point pairs are eliminated to ensure matching accuracy. Based on the matching results, the geometric transformation between the images is calculated. After aligning the multiple images, the pixel grayscale values ​​in overlapping areas are smoothed to eliminate stitching artifacts. Finally, an orthophoto basemap covering the operational area is synthesized. Finally, using the GPS location information (latitude and longitude) associated with each image, each pixel in the orthophoto basemap is assigned a corresponding geographic coordinate, creating a unified spatial reference basemap that serves as a reference for geometric correction of the hyperspectral data.

[0063] Step 4: Generate ortho-hyperspectral data.

[0064] (1) Block processing of hyperspectral data: (Block processing is because it is impossible for the flight strip to maintain a uniform speed throughout the entire flight, and the low GPS accuracy causes local deformation of the image. If one block of the same flight strip is accurately matched, another block farther away may not match so accurately.) The effective flight strip hyperspectral data after being cropped in step 2 is divided into several data blocks according to the preset size (such as width × width × spectral dimension, for example 640 × 640 × 300); the GPS positioning information corresponding to the middle frame of each data block is extracted and converted into UTM coordinates; the image corresponding to each small block on the orthophoto base map is intercepted by combining the geographic pixel size of the hyperspectral image and the visible light orthophoto base map. The hyperspectral geographic pixel size can be obtained by averaging the distance between two frames of GPS data along the flight line. The approximate UTM coordinates of the four corners of the cropped hyperspectral block can be calculated by the UTM coordinates of the center block and the geographic pixel size. Because each pixel of the visible light orthophoto image corresponds to a UTM coordinate, the pixel position can be found by the UTM coordinate, and then a slightly larger area is intercepted according to the geographic range of the data block (such as reserving 10% matching redundancy). Because GPS accuracy may not be high, this step is a rough screening to improve matching speed with the help of geographic information.

[0065] (2) Image matching and transformation matrix acquisition: Select three channels of the block-based hyperspectral data to synthesize the visible light image or select a channel with a high signal-to-noise ratio as the matching image. Use the same method (superpoint, etc.) to extract feature points and perform feature point matching with the visible light orthophoto image block corresponding to the geographic coordinates of the hyperspectral image block obtained in (1). Obtain the transformation matrix based on the matching relationship and save it.

[0066] (3) Orthophoto hyperspectral image generation: Traverse all hyperspectral data blocks, apply the corresponding spatial transformation matrix to the two-dimensional data array of each wavelength dimension, and finally generate a complete orthophoto hyperspectral image with UTM geographic coordinate attributes, realizing the precise association between spectral data and geographic space.

[0067] An automatic clipping and correction airborne hyperspectral data processing system adopts the automatic clipping and correction airborne hyperspectral data processing method, characterized by comprising a data acquisition module, an automatic flight strip clipping module, a geographic reference benchmark construction module and an orthophoto hyperspectral data generation module;

[0068] The data acquisition module is responsible for collecting data using multi-source sensors such as hyperspectral cameras, visible light cameras, GPS, and zenithal light equipment according to the predetermined route;

[0069] The automatic flight strip cutting module is used to automatically identify core routes based on the trajectory clustering algorithm, select qualified track clusters as target route segments, and complete the refined screening and final cutting of flight strips;

[0070] The georeferenced benchmark construction module is used to pre-process the visible light image to eliminate optical distortion, adjust the brightness and contrast, and synthesize the orthophoto map. It also uses GPS information to assign geographic coordinates to each pixel of the map to form a unified spatial benchmark.

[0071] The ortho-hyperspectral data generation module is used to segment the acquired valid flight strip data into small blocks and perform preliminary matching between GPS and the ortho-map. It then extracts and matches feature points to generate transformation matrices. Finally, these matrices are applied to the data at each wavelength dimension to generate ortho-hyperspectral images with UTM coordinates.

[0072] The automatic track clipping technology of the present invention extracts and clips effective flight strips by performing algorithmic analysis on GPS trajectory data and combining information such as latitude, longitude, heading, and altitude. On the one hand, it can remove redundant spectral data, significantly reduce the amount of subsequent calculations, significantly improve data processing speed, and solve the problem of low efficiency of traditional manual clipping. On the other hand, by accurately extracting effective flight strips, it avoids the omission of valid data or redundant residual caused by manual operation, provides high-quality basic data for subsequent correction processing, and ensures the accuracy of overall data processing.

[0073] The spectral data correction technology based on GPS and orthophotos eliminates the need for high-precision IMU modules. Instead, visible light cameras capture photos and process them into orthophotos. GPS data is then used for projection conversion and registration, and geometric calibration is achieved through a transformation matrix. Compared to traditional IMU-based correction methods, this significantly reduces hardware costs, making hyperspectral data processing more economical and accessible. Furthermore, by combining accurate data automatically cropped in the early stages, the correction process reduces redundant information interference, further improving the geometric accuracy and reliability of the spectral data.

[0074] The synergy between automatic track clipping and GPS-orthophoto correction technology has formed an efficient and complete processing flow. The high-quality data after automatic clipping provides more accurate input for the correction process, reducing the sources of error in the correction process. The low-cost advantage of the correction technology enables the processing solution to be applied in more scenarios. The combination of the two not only improves the efficiency and accuracy of data processing, but also lowers the application threshold, helping airborne hyperspectral data to play a greater role in water quality monitoring, ecological assessment and other fields.

[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for processing airborne hyperspectral data with automatic cropping and correction, characterized in that: The following steps are involved: Step S1: Airborne multi-source data acquisition: Using a hyperspectral camera, visible light camera, GPS, and zenithal light equipment, data is collected according to the planned route. The heading angle is deduced based on the changes in longitude and latitude at adjacent moments. The zenithal light equipment monitors the changes in ambient light in real time, and the exposure time of the hyperspectral camera is adjusted in real time based on the collected data. The reflectivity is calculated in real time using the zenithal light data and hyperspectral data. Step S2: Automatic flight strip cutting: using the trajectory clustering algorithm to identify the core route, screening the qualified track clusters as the target route segments, generating a complete operation flight strip and completing the cutting; Step S3: constructing a georeferenced benchmark, preprocessing the visible light image, synthesizing an orthophoto map, and using GPS information to assign geographic coordinates to each pixel of the map to form a georeferenced map; Step S4: Orthophoto hyperspectral data generation: divide the valid flight strip data into small blocks, perform preliminary matching based on GPS and orthophoto basemap, extract feature points and match them to obtain the transformation matrix, apply the transformation matrix to each wavelength dimension, and generate an orthophoto hyperspectral image with UTM coordinates.

2. The method for processing airborne hyperspectral data with automatic cropping and correction according to claim 1, characterized in that: In step S1, data is collected according to the planned route in the following manner: The hyperspectral camera, zenithal light, and GPS collect data at the same frequency. The GPS obtains the current timestamp and latitude and longitude information in real time. Each frame of the hyperspectral camera and zenithal light corresponds to a set of GPS data. Each visible light image obtains the corresponding GPS data through the relationship between the acquisition frequencies and the timestamp.

3. The method for processing airborne hyperspectral data with automatic cropping and correction according to claim 1, characterized in that: The heading angle in step S1 is derived in the following manner: Based on the temporal changes of longitude and latitude at adjacent moments, the heading angle of each frame of data is derived using the spherical azimuth formula and the result is converted into an angle value from 0° to 360°. The inherent parameters of the equipment are exported as data for automatic flight strip clipping and geometric correction.

4. The method for processing airborne hyperspectral data with automatic cropping and correction according to claim 1, characterized in that: In step S2, the core routes are identified by: The spatial neighborhood radius and the minimum number of points that are suitable for the spacing between operating routes are set. By calculating the spatial density distribution of trajectory points, spatially continuous clustered trajectory points with a number of points greater than or equal to the minimum number in the neighborhood are divided into the same track cluster. The three track clusters containing the longest continuous track points are selected, and the average heading angle of each cluster is calculated. Starting from the longest route, if there is a track cluster whose heading is ±180° opposite to that of any of the other two clusters, then this cluster is selected as the reference route.

5. The method for processing airborne hyperspectral data with automatic cropping and correction according to claim 4, characterized in that: In step S2, the qualified track clusters are selected as target route segments in the following manner: Taking the determined reference route as a reference, traverse all remaining track clusters, filter out the track clusters whose length exceeds the set threshold and whose heading angle deviation is greater than the reference route, mark them as target route segments, and exclude track clusters whose deviation exceeds the limit or whose length is insufficient.

6. The method for processing airborne hyperspectral data with automatic cropping and correction according to claim 5, characterized in that: The step S2 also includes fine-grained screening of flight strips in the following manner: The velocity parameters calculated based on the average elevation and longitude and latitude changes of each track cluster are used to eliminate discrete points. A sliding window is set by linear fitting to check the slope stability of the trajectory within the window and the distance between adjacent points. Points with abnormal slopes or distances exceeding a certain range of the average distance are removed from the target label, and continuous trajectory segments with stable motion states are retained.

7. The method for processing airborne hyperspectral data with automatic cropping and correction according to claim 6, characterized in that: In step S2, a complete operation strip is generated and cut out by: Integrate all target label route segments that have passed the refined screening to generate a complete and effective operation strip. Based on business needs, cut out all track segments within the specified label range from the complete strip, filter out the cutting range of the effective strip, and complete automatic strip cutting.

8. The method for processing airborne hyperspectral data with automatic cropping and correction according to claim 1, characterized in that: The formation of the georeferenced map in step S3 is carried out in the following manner: Image distortion caused by lens optical distortion is eliminated based on the camera's own parameters. The brightness and contrast of the image are then adjusted under different lighting conditions. Cross-image matching is performed by comparing the similarity of feature points between different images. The geometric transformation relationship between the images is calculated based on the matching results. After aligning multiple images, the pixel grayscale values ​​in the overlapping areas are smoothly transitioned to form an orthophoto map. By using the GPS positioning information associated with each image when it was taken, each pixel of the orthophoto basemap is assigned corresponding geographic coordinates to form a geographic reference basemap with a unified spatial benchmark.

9. The method for processing airborne hyperspectral data with automatic cropping and correction according to claim 1, characterized in that: The orthophoto hyperspectral data in step S4 is generated by: Step A1: Block processing of hyperspectral data. The effective flight zone hyperspectral data is divided into several data blocks according to a preset size. The GPS positioning information corresponding to the middle frame of each data block is extracted and converted to UTM coordinates. The image corresponding to each small block on the orthophoto basemap is cut out by combining the hyperspectral image and the geographic pixel size of the visible light orthophoto basemap. The approximate UTM coordinates corresponding to the four corners of the cropped hyperspectral block can be calculated based on the UTM coordinates of the center block and the geographic pixel size. The pixel position is found using the UTM coordinates, and a slightly larger area is cut out based on the geographic range of the data block. Step A2: Image matching and transformation matrix acquisition: Select three channels from the segmented hyperspectral data to synthesize a visible light image, or select a channel with a high signal-to-noise ratio as the matching image. Extract feature points from the obtained visible light orthophoto image blocks corresponding to the geographic coordinates of the hyperspectral image blocks, perform feature point matching, and obtain and save the transformation matrix based on the matching relationship. Step A3: Generate orthophoto hyperspectral images. Traverse all hyperspectral data blocks and apply the corresponding spatial transformation matrix to the two-dimensional data array of each wavelength dimension to generate a complete orthophoto hyperspectral image with UTM geographic coordinate attributes, thus completing the precise association between spectral data and geographic space.

10. An automatic cropping and correction airborne hyperspectral data processing system, using the automatic cropping and correction airborne hyperspectral data processing method according to any one of claims 1 to 9, characterized in that: It includes data acquisition module, automatic flight strip clipping module, georeferenced benchmark construction module and orthophoto hyperspectral data generation module; The data acquisition module is responsible for collecting data using multi-source sensors such as hyperspectral cameras, visible light cameras, GPS, and zenithal light equipment according to the predetermined route; The automatic flight strip cutting module is used to automatically identify core routes based on the trajectory clustering algorithm, select qualified track clusters as target route segments, and complete the refined screening and final cutting of flight strips; The georeferenced benchmark construction module is used to pre-process the visible light image to eliminate optical distortion, adjust the brightness and contrast, and synthesize the orthophoto map. It also uses GPS information to assign geographic coordinates to each pixel of the map to form a unified spatial benchmark. The orthophoto hyperspectral data generation module is used to divide the obtained valid flight strip data into small blocks, perform preliminary matching based on GPS and orthophoto basemap, and then obtain the transformation matrix through feature point extraction and matching. Finally, these matrices are applied to the data of each wavelength dimension to generate orthophoto hyperspectral images with UTM coordinates.

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