A method, system and device for geo-locating very large wide-area satellite imagery

Abstract

The application discloses a kind of super large wide satellite image geographic positioning method, system and equipment, method includes: obtaining satellite data, the ideal positioning of instrument;Select the first instrument image of same landmark in multiple days, the first geometric parameter calibration processing is carried out to first instrument image, obtain the first geometric parameter after calibration;Select the second instrument image of global long time sequence, combined with the second instrument image and the first geometric parameter after calibration is carried out second geometric parameter calibration processing, the second geometric parameter after calibration is input into positioning model;Positioning model carries out positioning accuracy test to second instrument image data, test result is fed back to global long time sequence second instrument image, and second geometric parameter carries out iteration.The application eliminates the edge distortion caused by super large width by calibrating preliminary geometric parameter to super large width image single landmark multi-time image, and then accurately calibrating geometric parameter through long time sequence global image together.

Description

Technical Field

[0001] This invention belongs to the field of satellite space, and more specifically, relates to a geolocation method, system and device for ultra-wide-angle satellite imagery. Background Technology

[0002] Remote sensing imagery is fundamental to remote sensing and many other applications. As spatial data, remote sensing images convey the concept of spatial geographic location. Regardless of the application problem, it is essential to determine the correspondence between image information and ground location. Geolocation of satellite imagery is a crucial prerequisite for satellite data preprocessing.

[0003] Geolocation of remote sensing images involves calculating the coordinates of images observed by spaceborne remote sensing instruments in a ground-based coordinate system. Existing geolocation methods are designed for high-resolution instruments with swath widths up to 1000 km, such as linear and area arrays. Geolocation methods designed for medium-resolution instruments with ultra-wide swaths remain scarce. The geolocation method proposed in this paper can effectively eliminate errors caused by edge field-of-view distortion effects that occur with ultra-wide swaths, such as those exceeding 2000 km.

[0004] Therefore, overcoming the technical defects of the existing technology is an urgent problem to be solved in this technical field. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a geolocation method, system and device for ultra-wide satellite imagery. Its purpose is to solve the error problem caused by edge field distortion effect by correcting the errors of satellite imagery along the orbit direction, along the scanning direction and the scanning mirror.

[0006] To achieve the above objectives, according to one aspect of the present invention, a geolocation method for ultra-wide-angle satellite imagery is provided, the method comprising:

[0007] Acquire satellite data to achieve ideal positioning for ultra-wide-swath instruments;

[0008] Select the first ultra-wide-angle instrument image of the same landmark within multiple days, perform first geometric parameter calibration processing on the first ultra-wide-angle instrument image, and obtain the calibrated first geometric parameters;

[0009] Select the second ultra-wide field image of the global long time series, combine the second ultra-wide field image with the calibrated first geometric parameters to perform second geometric parameter calibration, and input the calibrated second geometric parameters into the positioning model;

[0010] The positioning model verifies the positioning accuracy of the second ultra-wide swath instrument image data, feeds back the verification results to the global long-term series of the second ultra-wide swath instrument image, and iterates the second geometric parameters.

[0011] As a further improvement and supplement to the above solution, the present invention also includes the following additional technical features.

[0012] Preferably, the geometric parameter calibration includes:

[0013] After extracting the reference channel radiation value and the corresponding latitude and longitude, the first positioning accuracy test is performed to obtain the error along the track direction;

[0014] The image after correcting the deviation along the track is repositioned, and then a second positioning accuracy check is performed to obtain the error along the scanning direction;

[0015] The relationship between the scanning angle and the positioning error along the scanning direction is fitted, and distortion correction is performed on the edge of the field of view along the scanning direction.

[0016] The image after correcting the distortion at the edge of the scanning direction field of view is repositioned, and then a third positioning accuracy check is performed to obtain the angle error of the scanning mirror.

[0017] The ultra-wide satellite imagery after correcting for scanning mirror errors is repositioned, and then a fourth positioning accuracy test is performed to produce the positioning accuracy test results.

[0018] Preferably, after selecting the second ultra-wide satellite imagery of a global long-term series, cloud identification is performed using the Otsu threshold method, and the second ultra-wide satellite imagery is divided into cloud regions and non-cloud regions. The non-cloud regions are then selected for geometric parameter calibration.

[0019] Preferably, the method for acquiring satellite data and performing ideal instrument positioning includes:

[0020]

[0021] Where x and y are the coordinates on the focal plane, f is the focal length, and m is the correction factor. This is the mounting matrix for the scanning mirror to be installed in this system. This is the transformation matrix from the system to the inertial frame. This is the transformation matrix from the inertial frame to the Earth-fixed frame. , The satellite position vector matrix, The vector is the intersection of the view vector and the Earth's ellipse. The satellite position vector matrix and the vector intersecting the view vector and the Earth's ellipse are known from the satellite data.

[0022] Preferably, the method for performing a first positioning accuracy check after extracting the reference channel radiation value and the corresponding latitude and longitude to obtain the error along the track direction includes:

[0023]

[0024] in, x is the coordinate along the track direction, and Δµ is the error along the track direction. The corrected orbital coordinates, satellite position vector matrix, and the vector intersecting the Earth's ellipse are known from satellite data.

[0025] Preferably, the method for correcting distortion at the edge of the scanning direction field of view by fitting the relationship between the scanning angle and the positioning error along the scanning direction includes:

[0026] ;

[0027] in, Indicates the fitted scan angle. This represents the true scanning angle of the scanning mirror in the instrument, and α represents the fitting coefficient. n The coefficients of the fit for the nth degree polynomial are represented.

[0028] Preferably, the method for repositioning the image after correcting for scanning mirror errors includes:

[0029]

[0030] in, .

[0031] Preferably, the method for iterating the second geometric parameter includes:

[0032] The along-track error and cross-track error of the second ultra-wide-angle instrument image are statistically analyzed. The iteration stops when the deviation between the statistical values ​​of the along-track error and the cross-track error is less than 0.1 pixels.

[0033] Secondly, the present invention provides a geolocation system for ultra-wide-angle satellite imagery, the system comprising:

[0034] The data acquisition module is used to acquire satellite data;

[0035] The positioning module is used for ideal positioning of the instrument.

[0036] The positioning accuracy testing module is used to test the positioning accuracy of the instrument.

[0037] The calibration module is used to calibrate the geometric parameters of the instrument images;

[0038] The feedback module is used to iterate the geometric parameters.

[0039] Thirdly, the present invention provides a geolocation device for ultra-wide-angle satellite imagery, the device comprising:

[0040] One or more processors;

[0041] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the geolocation device method for ultrawide satellite imagery as described in the first aspect.

[0042] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0043] This invention provides a geolocation method, system, and device for ultra-wide satellite imagery. The invention eliminates edge distortion caused by ultra-wide imagery by calibrating preliminary geometric parameters for single-landmark multi-time images of ultra-wide imagery, and then by calibrating precise geometric parameters for long-time global images. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0045] Figure 1 This is a schematic diagram of a geolocation method for ultra-wide-angle satellite imagery provided in Embodiment 1.

[0046] Figure 2 This is a schematic diagram of global long-term instrument image along-track accuracy monitoring provided in this embodiment 1;

[0047] Figure 3 This is a schematic diagram of cross-track accuracy monitoring of global long-term instrument images provided in this embodiment 1;

[0048] Figure 4 This is a statistical diagram of the histogram of global long-term instrument image accuracy along the track, provided in Embodiment 1.

[0049] Figure 5 This is a statistical diagram of the cross-track accuracy histogram of global long-term instrument imagery provided in Embodiment 1;

[0050] Figure 6 This is a geolocation system for ultra-wide-swath satellite imagery provided in Embodiment 2;

[0051] Figure 7This is a geolocation device for ultra-wide satellite imagery provided in Embodiment 3. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0053] Example 1

[0054] This embodiment provides a geolocation method for ultra-wide-angle satellite imagery, such as... Figure 1 As shown, the method includes:

[0055] S101: Acquire satellite data for ideal positioning of ultra-wide swath instruments.

[0056] Acquiring satellite data requires data extraction, specifically extracting the reference channel radiation values ​​and their corresponding latitude and longitude.

[0057] The overall process for ideal positioning of ultra-wide-swath instruments includes:

[0058] Step 1: Acquire time data, orbit data, and attitude data to prepare for positioning;

[0059] Step 2: Perform quality checks on the time, orbit data, attitude data, etc. obtained from the positioning data preparation module, and generate checked orbit data, attitude data, and quality labels;

[0060] Step 3: Calculate the instrument coordinate system view vector based on the precise time of each pixel and the scanning angle of each scanned pixel;

[0061] Step 4: Based on the scanning observation time, orbit data, attitude data, instrument coordinate system view vector, preprocessed static data, etc., complete the geolocation calculation of the Earth observation data with the support of the public geolocation subsystem, and generate geolocation results and pixel-by-pixel terrain-corrected geodetic height according to the elevation static data process elevation correction.

[0062] Step 5: Generate geolocation-related files.

[0063] S102: Select the first ultra-wide-angle instrument image of the same landmark within multiple days, perform first geometric parameter calibration processing on the first ultra-wide-angle instrument image, and obtain the calibrated first geometric parameters.

[0064] The first geometric parameter calibration is performed on the first ultra-wide field instrument image. The first geometric parameter calibration includes:

[0065] After extracting the reference channel radiation value and corresponding latitude and longitude of the first ultra-wide-angle instrument image, the first positioning accuracy test is performed to obtain the error of the first ultra-wide-angle instrument image along the track direction.

[0066] The image after correcting the deviation along the track is repositioned, and then a second positioning accuracy test is performed to obtain the error of the first ultra-wide instrument image along the scanning direction.

[0067] The relationship between the scanning angle and the positioning error along the scanning direction is fitted, and the first ultra-wide instrument image distortion correction is performed on the edge of the field of view in the scanning direction.

[0068] The first ultra-wide instrument image after correcting the distortion at the edge of the scanning direction field of view is repositioned, and then the positioning accuracy is checked a third time to obtain the angle error of the scanning mirror.

[0069] The first ultra-wide satellite image after correcting the scanning mirror error is repositioned, and then a fourth positioning accuracy test is performed to issue the positioning accuracy test result.

[0070] S103: Select the second ultra-wide field image of the global long-term sequence, combine the second ultra-wide field image with the calibrated first geometric parameters to perform second geometric parameter calibration processing, and input the calibrated second geometric parameters into the positioning model.

[0071] The second geometric parameter calibration is performed on the second ultra-wide field instrument image. The second geometric parameter calibration includes:

[0072] After extracting the reference channel radiation value and corresponding latitude and longitude of the second ultra-wide-angle instrument image, the first positioning accuracy test is performed to obtain the error of the second ultra-wide-angle instrument image along the track direction.

[0073] The image after correcting the deviation along the track is repositioned, and then a second positioning accuracy check is performed to obtain the error of the second super-wide instrument image along the scanning direction.

[0074] The relationship between the fitted scanning angle and the positioning error along the scanning direction is used to correct the second super-wide instrument image distortion at the edge of the field of view along the scanning direction.

[0075] The second ultra-wide instrument image, after correcting the distortion at the edge of the scanning direction field of view, is repositioned, and then a third positioning accuracy test is performed to obtain the angle error of the scanning mirror.

[0076] The second ultra-wide satellite image, after correcting for scanning mirror errors, is repositioned, and then a fourth positioning accuracy test is performed to produce the positioning accuracy test results.

[0077] The positioning accuracy verification method used in this embodiment mainly includes the following steps:

[0078] The process involves: acquiring ground truth imagery of control points and their geographic location information; acquiring satellite imagery of the reference channel and its geographic location information; segmenting the satellite imagery using the Otsu thresholding method to obtain cloud and non-cloud region images; remapping the ground truth imagery using Gaussian filtering to obtain the remapped image; extracting feature points from the cloud and non-cloud region images and the remapped image; performing feature matching using the SIFT feature point matching algorithm to obtain matching point pairs; using the RANSAC algorithm to remove outlier matching point pairs to obtain exact matching point pairs; extracting the location information of the exact matching point pairs from the ground truth image geographic location information and the satellite image geographic location information, and converting it into positioning error; and verifying the positioning accuracy based on the positioning error to obtain the positioning accuracy verification result.

[0079] S104: The positioning model performs positioning accuracy verification on the second ultra-wide swath instrument image data, feeds back the verification result to the second ultra-wide swath instrument image of the global long-term series, and iterates the second geometric parameters.

[0080] In conjunction with the embodiments of the present invention, there is also a preferred implementation scheme, specifically, the geometric parameter calibration includes:

[0081] After extracting the reference channel radiation value and the corresponding latitude and longitude, the first positioning accuracy test is performed to obtain the error along the track direction;

[0082] The image after correcting the deviation along the track is repositioned, and then a second positioning accuracy check is performed to obtain the error along the scanning direction;

[0083] The relationship between the scanning angle and the positioning error along the scanning direction is fitted, and distortion correction is performed on the edge of the field of view along the scanning direction.

[0084] The image after correcting the distortion at the edge of the scanning direction field of view is repositioned, and then a third positioning accuracy check is performed to obtain the angle error of the scanning mirror.

[0085] The ultra-wide satellite imagery after correcting for scanning mirror errors is repositioned, and then a fourth positioning accuracy test is performed to produce the positioning accuracy test results.

[0086] In this first embodiment, the first geometric parameter calibration for the first ultra-wide-angle instrument image is set as: the first geometric parameter calibration algorithm for single-landmark multi-time instrument images.

[0087] The algorithm steps for calibrating the first geometric parameter of single-location, multi-time instrument imagery are as follows:

[0088] 1. Select instrument images of the same landmark over multiple days, generally choosing locations near the Red Sea where cloud cover is low.

[0089] 2. Process the prepared instrument images using the aforementioned geometric parameter calibration algorithm.

[0090] 3. Substitute the calibrated first geometric parameters into the new positioning model. The first geometric parameters include the angle error along the track, along the scanning direction, and the scanning mirror.

[0091] 4. Provide the positioning error analysis results.

[0092] In this first embodiment, the second geometric parameter calibration for the second ultra-wide-angle instrument image is set as: global long-time-series instrument image geometric parameter calibration algorithm.

[0093] The steps are as follows:

[0094] 1. Select long-term global instrument imagery and use the Otsu thresholding method for cloud identification. The Otsu thresholding method can automatically select the optimal threshold to achieve adaptive binarization segmentation of the image. The satellite image is automatically segmented into cloud regions and non-cloud regions.

[0095] 2. Process the prepared instrument images using the aforementioned geometric parameter calibration algorithm.

[0096] 3. Substitute the calibrated second geometric parameters into the new positioning model. The second geometric parameters include the angle error along the track, along the scanning direction, and the scanning mirror.

[0097] 4. Over time, continue to apply the algorithm to iteratively calibrate the second geometric parameters.

[0098] In conjunction with the embodiments of the present invention, there is also a preferred implementation scheme. Specifically, after selecting the second ultra-wide satellite image of the global long-term series, cloud identification is performed using Otsu thresholding, the second ultra-wide satellite image is divided into cloud regions and non-cloud regions, and the non-cloud regions are selected for geometric parameter calibration.

[0099] In conjunction with the embodiments of the present invention, there is also a preferred implementation scheme. Specifically, the method for acquiring satellite data and performing ideal instrument positioning includes:

[0100]

[0101] Where x and y are the coordinates on the focal plane, f is the focal length, and m is the correction factor. This is the mounting matrix for the scanning mirror to be installed in this system. This is the transformation matrix from the system to the inertial frame. This is the transformation matrix from the inertial frame to the Earth-fixed frame. , The satellite position vector matrix, The vector is the intersection of the view vector and the Earth's ellipse. The satellite position vector matrix and the vector intersecting the view vector and the Earth's ellipse are known from the satellite data.

[0102] In conjunction with the embodiments of the present invention, there is also a preferred implementation scheme. Specifically, after extracting the reference channel radiation value and the corresponding latitude and longitude, the method for performing the first positioning accuracy check to obtain the error along the track direction includes:

[0103]

[0104] in, x is the coordinate along the track direction, and Δµ is the error along the track direction. The corrected orbital coordinates, satellite position vector matrix, and the vector intersecting the Earth's ellipse are known from satellite data.

[0105] In conjunction with the embodiments of the present invention, there is also a preferred implementation scheme. Specifically, after calculating the error along the track direction, the error across the track direction is calculated, and the method includes:

[0106]

[0107] in, y is the coordinate in the cross-track direction, and Δµ2 is the error in the cross-track direction. The corrected transorbital direction coordinates, the satellite position vector matrix, and the vector intersecting the Earth's ellipse are known from the satellite data.

[0108] In conjunction with the embodiments of the present invention, there is also a preferred implementation scheme. Specifically, the method for correcting distortion at the edge of the scanning direction field of view by fitting the relationship between the scanning angle and the positioning error along the scanning direction includes:

[0109] ;

[0110] in, Indicates the fitted scan angle. This represents the true scanning angle of the scanning mirror in the instrument, and α represents the fitting coefficient. n The coefficients of the fit for the nth degree polynomial are represented.

[0111] Methods for iterating over the second geometric parameter include:

[0112] In conjunction with the embodiments of the present invention, there is also a preferred implementation scheme, specifically, the along-track error and cross-track error of the second ultra-wide-angle instrument image are statistically analyzed, and the iteration is stopped when the deviation between the statistical values ​​of the along-track error and the cross-track error is less than 0.1 pixels.

[0113] like Figure 2 and Figure 3 As shown, the blue dots represent the mean, and the red lines represent the standard deviation. The accuracy along the track and across the track is better than 1 pixel, proving that the geolocation method of ultra-wide satellite imagery provided in this embodiment can achieve the effect of eliminating edge distortion caused by ultra-wide swath.

[0114] like Figure 4 and Figure 5 As shown, according to the histogram statistics, the mean error along the track direction is 0.02 pixels, the standard deviation (STD) is 0.61 pixels, and the root mean square error (RMSE) is 0.61 pixels. The mean error across the track direction is 0.11 pixels, the standard deviation (STD) is 0.6 pixels, and the RMSE is 0.61 pixels. This demonstrates the statistical stability of the algorithm in both time and space.

[0115] Global statistical analysis of geolocation errors along and across tracks reveals a relatively consistent global trend, with no significant variations along latitude or longitude. The stability of the algorithm in both time and space can be observed from the projection map.

[0116] Example 2:

[0117] This second embodiment provides a geolocation system for ultra-wide-angle satellite imagery, such as... Figure 6 As shown, the system includes:

[0118] The data acquisition module is used to acquire satellite data;

[0119] The positioning module is used for ideal positioning of the instrument.

[0120] The positioning accuracy verification module is used to verify the positioning accuracy of ultra-wide-swath satellite imagery instruments.

[0121] The calibration module is used to calibrate the geometric parameters of ultra-wide-swath satellite imagery.

[0122] The feedback module is used to iterate the geometric parameters.

[0123] In this second embodiment, the geolocation system for ultra-wide-angle satellite imagery solves the current technical problem of errors caused by edge field distortion by correcting errors in the satellite imagery along the orbit direction, along the scanning direction, and in the scanning mirror.

[0124] Example 3

[0125] This third embodiment provides a geolocation device for ultra-wide-angle satellite imagery, such as... Figure 7 As shown, the device includes:

[0126] One or more processors;

[0127] A storage device for storing one or more programs that, when executed by one or more processors, enable the one or more processors to implement the geolocation device method for ultra-wide-angle satellite imagery as provided in Embodiment 1.

[0128] Figure 7 This is a schematic diagram of the geolocation device structure for ultra-wide-angle satellite imagery provided in this embodiment 2. Figure 7 A block diagram of an exemplary geolocation device suitable for implementing embodiments of the present invention using ultra-wide-swath satellite imagery is shown. Figure 7 The geolocation device for the ultra-wide satellite imagery shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0129] like Figure 7 As shown, geolocation equipment for ultra-wide-swath satellite imagery is presented in the form of general-purpose equipment. Components of geolocation equipment for ultra-wide-swath satellite imagery may include, but are not limited to: one or more processors or processing units, memory, and buses connecting different system components (including memory and processing units).

[0130] A bus refers to one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0131] Geolocation equipment for ultra-wide-swath satellite imagery typically includes a variety of computer-readable media. These media can be any available media that can be accessed by a smart logging interpretation model, including volatile and non-volatile media, and movable and immovable media.

[0132] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory. Geolocation equipment for ultra-wide-angle satellite imagery may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media (…). Figure 7 Not shown; usually referred to as a "hard drive"). Although Figure 7Not shown, disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to a bus via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0133] A program / utility having a set (at least one) of program modules can be stored, for example, in memory. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this invention.

[0134] The geolocation device for ultra-wide-swath satellite imagery can also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable users to interact with the geolocation device, and / or any device that enables the geolocation device to communicate with one or more other devices (e.g., network cards, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the device for correcting intelligent well logging interpretation models can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapters. Figure 7 As shown, the network adapter communicates with other modules of the geolocation device for ultra-wide-swath satellite imagery via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the geolocation device for ultra-wide-swath satellite imagery, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0135] The processing unit executes various functional applications and data processing by running programs stored in memory, such as implementing the geolocation method for ultra-wide-swath satellite imagery provided in any embodiment of the present invention. Specifically: it acquires satellite data and performs ideal instrument positioning; it selects a first instrument image of the same landmark over multiple days, performs first geometric parameter calibration processing on the first instrument image, and obtains the calibrated first geometric parameters; it selects a second instrument image from a global long-term series, combines the second instrument image with the calibrated first geometric parameters to perform second geometric parameter calibration processing, and inputs the calibrated second geometric parameters into the positioning model; the positioning model verifies the positioning accuracy of the second instrument image data, feeds back the verification result to the second instrument image from the global long-term series, and iterates the second geometric parameters.

[0136] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A geolocation method for ultra-wide-angle satellite imagery, characterized in that, The method includes: Acquire satellite data to achieve ideal positioning for ultra-wide-swath instruments; Select the first ultra-wide-angle instrument image of the same landmark within multiple days, perform first geometric parameter calibration processing on the first ultra-wide-angle instrument image, and obtain the calibrated first geometric parameters; Select the second ultra-wide field image of the global long-term series, combine the second ultra-wide field image with the calibrated first geometric parameters to perform second geometric parameter calibration, and input the calibrated second geometric parameters into the positioning model; The geometric parameter calibration includes: After extracting the reference channel radiation value and the corresponding latitude and longitude, the first positioning accuracy test is performed to obtain the error along the track direction; The image after correcting the deviation along the track is repositioned, and then a second positioning accuracy check is performed to obtain the error along the scanning direction; The relationship between the scanning angle and the positioning error along the scanning direction is fitted, and distortion correction is performed on the edge of the field of view along the scanning direction. The image after correcting the distortion at the edge of the scanning direction field of view is repositioned, and then a third positioning accuracy check is performed to obtain the angle error of the scanning mirror. The ultra-wide satellite image after correcting the scanning mirror error is repositioned, and then a fourth positioning accuracy test is performed to issue the positioning accuracy test results. The positioning model verifies the positioning accuracy of the second ultra-wide swath instrument image data, feeds back the verification results to the global long-term series of the second ultra-wide swath instrument image, and iterates the second geometric parameters.

2. The geolocation method for ultra-wide-swath satellite imagery as described in claim 1, characterized in that, After selecting the second ultra-wide satellite imagery of the global long-term series, cloud identification is performed using the Otsu threshold method. The second ultra-wide satellite imagery is then divided into cloud regions and non-cloud regions, and the non-cloud regions are selected for geometric parameter calibration.

3. The geolocation method for ultra-wide-swath satellite imagery as described in claim 2, characterized in that, The method for acquiring satellite data and performing ideal instrument positioning includes: ； Where x and y are the coordinates on the focal plane, f is the focal length, and m is the correction factor. This is the mounting matrix for the scanning mirror to be installed in this system. This is the transformation matrix from the system to the inertial frame. This is the transformation matrix from the inertial frame to the Earth-fixed frame. , The satellite position vector matrix, The vector is the intersection of the view vector and the Earth's ellipse. The satellite position vector matrix and the vector intersecting the view vector and the Earth's ellipse are known from the satellite data.

4. The geolocation method for ultra-wide-swath satellite imagery as described in claim 3, characterized in that, The method for obtaining the error along the track direction by performing the first positioning accuracy check after extracting the reference channel radiation value and the corresponding latitude and longitude includes: in, x is the coordinate along the track direction, and Δµ is the error along the track direction. The corrected orbital coordinates, satellite position vector matrix, and the vector intersecting the Earth's ellipse are known from satellite data.

5. The geolocation method for ultra-wide-swath satellite imagery as described in claim 4, characterized in that, The method for correcting distortion at the edge of the scanning direction field of view by fitting the relationship between the scanning angle and the positioning error along the scanning direction includes: ； in, Indicates the fitted scan angle. This represents the true scanning angle of the scanning mirror in the instrument, and α represents the fitting coefficient. n The coefficients of the fit for the nth degree polynomial are represented.

6. The geolocation method for ultra-wide-angle satellite imagery as described in claim 5, characterized in that, The method for repositioning the image after correcting for scanning mirror errors includes: ； in, .

7. The geolocation method for ultra-wide-swath satellite imagery as described in any one of claims 1 to 6, characterized in that, The methods for iterating the second geometric parameter include: The along-track error and cross-track error of the second ultra-wide-angle instrument image are statistically analyzed. The iteration stops when the deviation between the statistical values ​​of the along-track error and the cross-track error is less than 0.1 pixels.

8. A geolocation system for ultra-wide-angle satellite imagery, characterized in that, The system is used to implement the method according to any one of claims 1 to 7, the system comprising: The data acquisition module is used to acquire satellite data; The positioning module is used for ideal positioning of the instrument. The positioning accuracy testing module is used to test the positioning accuracy of the instrument. The calibration module is used to calibrate the geometric parameters of the instrument images; The feedback module is used to iterate the geometric parameters.

9. A geolocation device for ultra-wide-angle satellite imagery, characterized in that the device... include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement a geolocation device method for ultrawide satellite imagery as described in any one of claims 1 to 7.

Images