Optical remote sensing data registration method of micro-nano satellite, storage medium and electronic equipment

Abstract

The application discloses a kind of optical remote sensing data registration methods of micro-nano satellite, storage medium and electronic equipment, it is related to satellite image processing technical field;Same-named point deviation data of registration band data and datum band data is acquired;The distribution of same-named point deviation data is counted, and representative data of same-named point deviation data is acquired;According to representative data, the difference parameter of registration band data relative to datum band data is determined;According to difference parameter, registration band data is registered to datum band data.This application is by the deviation data between band statistics, with the distribution characteristics of data to determine representative data as the representation of all same-named point deviation data, can quickly determine an effective difference parameter to adapt to the adjustment of all same-named point deviation, avoid point-by-point detailed analysis deviation, reduce processing period, can efficiently deal with the processing of a large amount of data, improve the quality of satellite shooting image same-named point registration.

Description

Technical Field

[0001] This application relates to the field of satellite image processing technology, and in particular to a method for optical remote sensing data registration, storage medium and electronic equipment for micro and nano satellites. Background Technology

[0002] Microsatellites and nanosatellites refer to satellites weighing less than 10 kg with practical functions. Driven by the development of advanced technologies and evolving needs, microsatellites and nanosatellites offer advantages such as small size, low power consumption, short development cycles, and the ability to form constellations, enabling them to complete many complex space missions at a lower cost. However, some satellites use non-aerospace-grade components, leading to unstable operation of certain modules during long-term on-orbit operation. This can result in discrepancies in the location of corresponding points in images acquired by onboard imaging instruments.

[0003] To address this positional difference, existing technologies propose registration methods based on principles such as polynomial transformation, spline function transformation, correction transformation, projection transformation, or similarity transformation, employing a series of complex algorithms to achieve registration of corresponding points. On the one hand, developing these algorithms requires significant effort and increases costs; on the other hand, when faced with large amounts of data to be processed, the processing cycle of these complex algorithms is long, failing to guarantee high efficiency, resulting in the low quality of current registration methods. Summary of the Invention

[0004] The main objective of this application is to provide a registration method, storage medium, and electronic device for optical remote sensing data of micro- and nano-satellites, aiming to solve the problem of low quality in existing registration methods for the positional differences of corresponding points in satellite-captured images.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a method for registering optical remote sensing data from micro / nano satellites, comprising the following steps:

[0007] Obtain the deviation data of corresponding points between the registration band data and the reference band data; wherein, the reference band data is any band data of the spectral data, and the registration band data is the band data of the spectral data other than the reference band data;

[0008] Statistically analyze the distribution of the deviation data of corresponding points and obtain representative data of the deviation data of corresponding points;

[0009] Based on representative data, determine the difference parameters between the registration band data and the reference band data;

[0010] Based on the difference parameters, the registered band data is registered to the reference band data.

[0011] In one possible implementation of the first aspect, the corresponding point deviation data includes the deviation value and the deviation direction.

[0012] In one possible implementation of the first aspect, the distribution of the corresponding point deviation data is statistically analyzed, and representative data of the corresponding point deviation data are obtained, including:

[0013] According to the order of the magnitude of the deviation values, the frequency of the same deviation value occurring in the deviation direction is counted to obtain representative data of the deviation data of the same point.

[0014] In one possible implementation of the first aspect, representative data of the deviation data of corresponding points are obtained by statistically analyzing the frequency of occurrence of the same deviation value in the deviation direction according to the order of the magnitude of the deviation value, including:

[0015] Count the frequency of the same deviation value occurring in the same deviation direction, in order of magnitude of the deviation value.

[0016] Based on the normal distribution of the statistical results, the deviation values ​​that occur most frequently in different deviation directions are obtained as representative data of the deviation data of the same point.

[0017] In one possible implementation of the first aspect, the registered band data is registered to the reference band data based on the difference parameters, including:

[0018] The registered band data is cropped based on the difference parameters;

[0019] The cropped registered band data is then registered to the reference band data.

[0020] In one possible implementation of the first aspect, after registering the registered band data to the reference band data based on the difference parameters, the optical remote sensing data registration method for microsatellites further includes:

[0021] The registered band data and the reference band data are fused together to obtain a fused image.

[0022] In one possible implementation of the first aspect, obtaining the corresponding point deviation data between the registration band data and the reference band data includes:

[0023] Obtain the pixel deviation data of the corresponding points between the registration band data and the reference band data;

[0024] Statistically analyze the distribution of the deviation data of corresponding points, and obtain representative data of the deviation data of corresponding points, including:

[0025] Statistically analyze the distribution of pixel deviation data for corresponding points and obtain representative data of the deviation data for corresponding points.

[0026] In one possible implementation of the first aspect, before acquiring the corresponding point deviation data between the registration band data and the reference band data, the optical remote sensing data registration method for microsatellites further includes:

[0027] Acquire spectral data in several bands; these bands include red band data, green band data, and blue band data.

[0028] In a second aspect, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the optical remote sensing data registration method for microsatellites as provided in any of the first aspects above.

[0029] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein...

[0030] Memory is used to store computer programs;

[0031] The processor is used to load and execute computer programs to cause electronic devices to perform optical remote sensing data registration methods for microsatellites as provided in any of the first aspects above.

[0032] Compared with the prior art, the beneficial effects of this application are:

[0033] This application proposes a method, storage medium, and electronic device for optical remote sensing data registration of micro-nano satellites. The method includes: acquiring the corresponding point deviation data between registration band data and reference band data; wherein the reference band data is any band data of spectral data, and the registration band data is band data of spectral data other than the reference band data; statistically analyzing the distribution of the corresponding point deviation data to obtain representative data of the corresponding point deviation data; determining the difference parameter between the registration band data and the reference band data based on the representative data; and registering the registration band data to the reference band data based on the difference parameter. The method in this application utilizes the characteristics of satellite spectral data to reduce algorithm complexity from the source. It transforms the image corresponding point deviation into the difference between corresponding points in each band of the spectral data, thereby reducing the dimensionality of the original data. The high-dimensional original data is decomposed into several band data for processing. Furthermore, the deviation data between bands is statistically analyzed, and representative data is determined based on the distribution characteristics of the data to represent the deviation data of all corresponding points. This allows for the rapid determination of an effective difference parameter to accommodate the adjustment of all corresponding point deviations, avoiding point-by-point detailed analysis of deviations, reducing the processing cycle, and efficiently handling the processing of large amounts of data, thus improving the quality of corresponding point registration for satellite-captured images. Attached Figure Description

[0034] Figure 1 A flowchart illustrating the optical remote sensing data registration method for microsatellites provided in this application embodiment;

[0035] Figure 2 This is a statistical chart showing the pixel deviation between the red band data and the blue band data in the row direction in this embodiment of the application.

[0036] Figure 3 This is a statistical chart showing the pixel deviation of red band data relative to blue band data in the column direction in this embodiment of the application.

[0037] Figure 4 This is a statistical chart showing the pixel deviation between green band data and blue band data in the row direction in this embodiment of the application.

[0038] Figure 5 This is a statistical chart showing the pixel deviation of green band data relative to blue band data in the column direction in this embodiment of the application.

[0039] Figure 6 This is a schematic diagram of the module of the optical remote sensing data registration device for micro-nano satellites involved in the embodiments of this application. Detailed Implementation

[0040] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0041] The main solution of this application embodiment is: a method for registering optical remote sensing data of micro-nano satellites, including: acquiring the corresponding point deviation data between the registration band data and the reference band data; wherein, the reference band data is any band data of the spectral data, and the registration band data is the band data of the spectral data other than the reference band data; statistically analyzing the distribution of the corresponding point deviation data, and obtaining representative data of the corresponding point deviation data; determining the difference parameter of the registration band data relative to the reference band data based on the representative data; and registering the registration band data to the reference band data based on the difference parameter.

[0042] Due to the rapid and low-cost development of microsatellites and nanosatellites, some satellites use non-aerospace-grade components, leading to unstable operation of certain modules during long-term on-orbit operation. This results in inconsistent geographical locations for data captured across different spectral bands. Therefore, image registration is necessary. Currently, there are two main registration methods: one is based on grayscale values, and the other is based on feature points.

[0043] The basic principle of the image grayscale matching method is to construct a similarity measurement function, and through a corresponding optimization search algorithm, find the transformation relationship between the reference image and the image to be registered, so that the similarity measurement function reaches an extreme value, and the registration is completed based on this function.

[0044] Feature-point-based registration generally employs the principle of corresponding point registration. Corresponding points in satellite imagery refer to the image points formed by the same point on the ground in different images. When collecting corresponding points to compare and analyze multi-source remote sensing images, geometric registration between the multiple source images is usually required. Multi-source images may originate from different remote sensing platforms, sensors, bands, orientations, and sensing methods. Determining the corresponding points between multi-source remote sensing images is crucial for image registration. When dealing with a large number of remote sensing images, the required number of corresponding points is substantial and cannot be determined manually; therefore, automatic registration is necessary.

[0045] Current registration methods based on corresponding points typically employ complex algorithms to determine the deviation between these points, perform point-by-point detailed analysis, and introduce transformations such as projection and affine transformations to calculate highly accurate values ​​to characterize the deviation, thereby achieving registration. On the one hand, the development of complex algorithms increases costs, contradicting the low-cost advantage of microsatellites; on the other hand, complex algorithms prolong the processing cycle, making registration methods ineffective for handling large datasets.

[0046] To address this, this application provides a solution that statistically analyzes the deviation data between bands and uses the data distribution characteristics to determine representative data as a representation of the deviation data for all corresponding points. This allows for the rapid determination of an effective difference parameter to accommodate adjustments for all corresponding point deviations, avoiding point-by-point detailed analysis of deviations, reducing the processing cycle, and efficiently handling large amounts of data, thus improving the quality of corresponding point registration for satellite-captured images. By utilizing the characteristics of satellite spectral data, the algorithm complexity is reduced from the outset. The image corresponding point deviation is transformed into the calculation of corresponding point differences between bands of spectral data, reducing the dimensionality of the original processed data and further improving registration efficiency.

[0047] The method in this application embodiment can be executed programmatically, based on an existing electronic device running a program. The electronic device can be a mobile phone, tablet, desktop computer, etc. The electronic device can include a processor, storage medium, etc. The storage medium is used to store the program for executing the method of this embodiment, and the processor is used to load and execute the computer program so that the electronic device executes the optical remote sensing data registration method for micro-nano satellites provided in this application embodiment.

[0048] See attached document Figure 1 Based on the hardware device described in the foregoing embodiments, embodiments of this application provide a method for optical remote sensing data registration of microsatellites and nanosatellites, comprising the following steps:

[0049] S00: Acquire several bands of spectral data; among which, the several bands of data include red band data, green band data and blue band data.

[0050] In the specific implementation process, the spectral data is image data obtained by satellite and stored in the RAW format with higher image quality. In this format, the data is stored separately in individual bands, that is, the spectral data is stored in the form of red band data, green band data and blue band data. The data information of the spectral data can be viewed through commonly used remote sensing data processing software such as ENVI and ArcGIS.

[0051] ENVI is a complete remote sensing image processing platform. Its suite of software processing technologies covers image data input / output, image calibration, image enhancement, correction, orthorectification, mosaicking, data fusion and various transformations, information extraction, image classification, knowledge-based decision tree classification, integration with GIS, DEM and terrain information extraction, radar data processing, and 3D stereoscopic display and analysis. The ArcGIS product line provides users with a scalable, comprehensive GIS platform that connects people, locations, and data through interactive maps, intelligent data-driven styles, and intuitive analysis tools.

[0052] S10: Obtain the deviation data of corresponding points between the registration band data and the reference band data; wherein, the reference band data is any band data of the spectral data, and the registration band data is the band data of the spectral data other than the reference band data.

[0053] In practical implementation, general registration can be divided into two types based on the registration object: one is to determine the same standard image and register all other images to the standard image; the other is to select one of the registration objects as the reference for registration. However, when dealing with large amounts of data, a standard image needs to be provided for each location of the image, which greatly increases the workload. In order to make the method of this application applicable to the processing of large amounts of data and to simplify the registration steps, any band data can be directly used as the reference, and other band data can be registered to it. The registration is also based on the registration of corresponding points. Taking the blue band data as the reference band data as an example, the corresponding point deviation data includes: the corresponding point deviation data between the green band data and the blue band data, and the corresponding point deviation data between the red band data and the blue band data.

[0054] S20: Statistically analyze the distribution of deviation data for corresponding points and obtain representative data for deviation data for corresponding points.

[0055] In the specific implementation process, to determine representative data based on the data distribution, statistical analysis is performed on the data to reflect the distribution of the deviation data of corresponding points. This allows for a more intuitive and convenient extraction of representative data to represent the entire deviation data, ensuring that the deviation of this representative data is relatively small compared to all other data. For example, the data can be statistically analyzed by arranging the data in ascending order of magnitude to obtain the median, mode, etc., as representative data; or the average, weighted average, etc., can be used as representative data. Since wavebands have directionality, the deviation data of corresponding points can include both the deviation value and the deviation direction. Therefore, during statistical analysis, data can be collected separately according to different deviation directions of the waveband, and representative data can be determined for each direction.

[0056] S30: Based on representative data, determine the difference parameters between the registration band data and the reference band data.

[0057] In the specific implementation process, since the acquired representative data can be used as a representative of the entire deviation data, that is, it can be used as a difference parameter to perform registration based on corresponding points, the difference parameter includes the direction and deviation value based on the band direction. For example, in the band line direction, how many units of deviation does the green band data have relative to the blue band data? The unit value depends on the specific situation and can be a size unit or a coordinate unit.

[0058] S40: Register the registered band data to the reference band data based on the difference parameters.

[0059] In practice, the difference parameters serve as the basis for registration, enabling the registration of the band data to the reference band data. This is achieved through techniques such as translation and cropping to bring the band data closer to the reference band data, thus realizing the registration of corresponding points. Specifically, based on the difference parameters, the registration of the band data to the reference band data includes:

[0060] S401: Crop the registered band data based on the difference parameters.

[0061] S402: Register the cropped registered band data to the reference band data.

[0062] Using the difference parameter, the registered band data is cropped. Different positions of each band are cropped according to the same standard, that is, the same difference parameter is subtracted to achieve the matching of corresponding points. For example, in step S30, the cropping is to subtract the difference parameter in the row direction of all positions of the green band data to achieve the registration of corresponding points.

[0063] S50: Perform band fusion on the registered band data and the reference band data to obtain a fused image.

[0064] In the specific implementation process, to restore the data into image form and more clearly present the registration effect, the band data is processed in true color so that the colors of the synthesized image approximate the true colors of the ground scene. The registered band data and the reference band data are then subjected to band fusion processing. Band fusion utilizes spectral band data to fuse them and reconstruct an image with true colors, i.e., a fused image. For example, Erdas software, a remote sensing image processing system, can be used to fuse multi-band data using its band fusion function to obtain a fused image.

[0065] In this embodiment, by statistically analyzing the deviation data between bands, representative data is determined based on the data distribution characteristics to characterize the deviation data of all corresponding points. This allows for the rapid identification of an effective difference parameter to accommodate the adjustment of deviations at all corresponding points, avoiding point-by-point detailed analysis of deviations, reducing the processing cycle, and efficiently handling large amounts of data, thus improving the quality of corresponding point registration for satellite-captured images. By utilizing the characteristics of satellite spectral data, the algorithm complexity is reduced from the outset. Image corresponding point deviations are transformed into the calculation of corresponding point differences between bands of spectral data, reducing the dimensionality of the original processed data and further improving registration efficiency.

[0066] In one embodiment, statistically analyzing the distribution of corresponding point deviation data and obtaining representative data of the corresponding point deviation data includes:

[0067] According to the order of the magnitude of the deviation values, the frequency of the same deviation value occurring in the deviation direction is counted to obtain representative data of the deviation data of the same point.

[0068] In the specific implementation process, after statistically analyzing the deviation values ​​in order of magnitude, and considering the frequency of each deviation value, the statistically analyzed deviation data exhibits a normal distribution. This more intuitively reflects the mode and peak values ​​of the deviations. To complement the normal distribution of the data, the frequency of the same deviation value along the deviation direction is statistically analyzed in order of magnitude to obtain representative data for the deviations at corresponding points, including:

[0069] Count the frequency of the same deviation value occurring in the same deviation direction, in order of magnitude of the deviation value.

[0070] Based on the normal distribution of the statistical results, the deviation values ​​that occur most frequently in different deviation directions are obtained as representative data of the deviation data of the same point.

[0071] As attached Figure 2-5The figures show the statistical results of different registration band data relative to the reference band data in different deviation directions. Following a normal distribution, the closer to the peak, the higher the probability of occurrence; therefore, data at the peak is the most representative. Registration based on this representative data can accommodate the registration of all corresponding points in the selected band direction and ensure high accuracy.

[0072] In one embodiment, step S10: obtaining the corresponding point deviation data between the registration band data and the reference band data, including:

[0073] S101: Obtain the pixel deviation data of the corresponding points between the registration band data and the reference band data.

[0074] In the specific implementation process, in order to extract the deviation data of corresponding points more accurately, the pixel deviation data of corresponding points is determined at the pixel level. This is done using image application software such as Photoshop and ENVI. For example, Photoshop can intuitively and quickly determine the deviation value at the pixel level, while ENVI can use its roll-out tool to determine the deviation direction.

[0075] Based on the aforementioned steps, the distribution of the corresponding point deviation data is statistically analyzed, and representative data of the corresponding point deviation data are obtained, including:

[0076] Statistically analyze the distribution of pixel deviation data for corresponding points and obtain representative data of the deviation data for corresponding points.

[0077] See attached document Figure 1-5 Using blue band data as the reference band data and red and green band data as the registration band data, this application is further explained as follows:

[0078] The information from the raw spectral data is read to obtain red, green, and blue band data, with a color depth of 16 bits.

[0079] Using Photoshop and ENVI software, we obtained the corresponding pixel deviation data of the red band data relative to the blue band data, and the corresponding pixel deviation data of the green band data relative to the blue band data. We then statistically analyzed the corresponding pixel deviations of the above bands in multiple image data as sample data before registration.

[0080] Statistical analysis was performed on the above data to obtain the distribution of pixel deviation data of corresponding points in the red band relative to the blue band data, and the distribution of pixel deviation data of corresponding points in the green band relative to the blue band data, as shown in the attached figure. Figure 2-4The histograms shown represent the pixel deviations of red band data relative to blue band data in the row direction, the pixel deviations of red band data relative to blue band data in the column direction, the pixel deviations of green band data relative to blue band data in the row direction, and the pixel deviations of green band data relative to blue band data in the column direction, respectively.

[0081] Based on the normal distribution of the data in their respective histograms, representative data were determined as the difference parameters; as shown in the appendix. Figure 2 , 3 As shown, the difference between the red band data and the blue band data in the row and column directions is 5 pixels and 3 pixels, respectively; see attached. Figure 4 , 5 As shown, the difference between the green band data and the blue band data in the row and column directions is 3 pixels and 3 pixels, respectively.

[0082] This section calls the GDAL and NumPy libraries to read spectral data, adjusts the spectral positional relationships between bands to ensure that corresponding points coincide, and completes registration. The GDAL library is an open-source raster spatial data conversion library under the X / MIT license. It is a cross-platform raster geographic data format library, including reading, writing, converting, and processing various raster data formats. GDAL uses an abstract data model to parse the data formats it supports. This abstract data model includes datasets, coordinate systems, affine geographic coordinate transformations, geodetic control points, metadata, raster bands, color tables, subset domains, image structure domains, and XML domains. The NumPy library supports advanced, high-dimensional array and matrix operations, and also provides a large library of mathematical functions for array operations.

[0083] The registered red, green, and blue band data were re-fused, and the color depth was reduced to 8 bits to obtain true color data that closely resembles real colors.

[0084] See attached document Figure 6 Based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide an optical remote sensing data registration device for micro / nano satellites, comprising:

[0085] The first acquisition module is used to acquire the deviation data of corresponding points between the registration band data and the reference band data; wherein, the reference band data is any band data of the spectral data, and the registration band data is the band data of the spectral data other than the reference band data.

[0086] The statistics module is used to analyze the distribution of deviation data at corresponding points and obtain representative data of the deviation data at corresponding points.

[0087] The second acquisition module is used to determine the difference parameters between the registration band data and the reference band data based on the representative data.

[0088] The registration module is used to register the registration band data to the reference band data based on the difference parameters.

[0089] It should be noted that each module in the optical remote sensing data registration device of the micro-nano satellite in this embodiment corresponds one-to-one with each step in the optical remote sensing data registration method of the micro-nano satellite in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned optical remote sensing data registration method of the micro-nano satellite, and will not be repeated here.

[0090] Furthermore, based on the same inventive concept as in the foregoing embodiments, embodiments of this application also provide a computer storage medium storing a computer program, which, when run by a processor, implements the steps of the optical remote sensing data registration method for micro-nano satellites in the foregoing embodiments.

[0091] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0092] The order of the embodiments described above is merely for illustrative purposes and does not represent the superiority or inferiority of the embodiments.

[0093] In summary, the optical remote sensing data registration method for micro-nano satellites described in this application includes: acquiring the corresponding point deviation data between the registration band data and the reference band data; wherein the reference band data is any band data of the spectral data, and the registration band data is the band data of the spectral data other than the reference band data; statistically analyzing the distribution of the corresponding point deviation data to obtain representative data of the corresponding point deviation data; determining the difference parameter of the registration band data relative to the reference band data based on the representative data; and registering the registration band data to the reference band data based on the difference parameter. This application, by statistically analyzing the deviation data between bands and using the distribution characteristics of the data to determine representative data as a representation of all corresponding point deviation data, can quickly determine an effective difference parameter to accommodate the adjustment of all corresponding point deviations, avoiding point-by-point detailed analysis of deviations, reducing the processing cycle, efficiently handling the processing of large amounts of data, and improving the quality of corresponding point registration for satellite-captured images. By utilizing the characteristics of satellite spectral data, the complexity of the algorithm is reduced from the source. The deviation of corresponding points in the image is transformed into the difference between corresponding points in each band of the spectral data, which reduces the dimensionality of the original data and further improves the efficiency of registration.

[0094] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for registering optical remote sensing data from micro / nano satellites, characterized in that, Includes the following steps: Obtain the deviation data of corresponding points between the registration band data and the reference band data; wherein, the reference band data is any band data of the spectral data, and the registration band data is the band data of the spectral data other than the reference band data; Statistically analyze the distribution of the corresponding point deviation data to obtain representative data of the corresponding point deviation data; Based on the representative data, determine the difference parameters between the registration band data and the reference band data; Based on the difference parameters, the registered band data is registered to the reference band data, including: The registered band data is cropped based on the difference parameters. The cropped registration band data is then registered with the reference band data.

2. The optical remote sensing data registration method for microsatellites according to claim 1, characterized in that, The deviation data of the corresponding points includes the deviation value and the deviation direction.

3. The optical remote sensing data registration method for microsatellites according to claim 2, characterized in that, The step of statistically analyzing the distribution of the corresponding point deviation data and obtaining representative data of the corresponding point deviation data includes: According to the order of the magnitude of the deviation values, the frequency of the same deviation value appearing in the deviation direction is counted to obtain representative data of the deviation data of the same point.

4. The optical remote sensing data registration method for microsatellites according to claim 3, characterized in that, The step of counting the frequency of the same deviation value occurring in the deviation direction according to the magnitude of the deviation value, and obtaining representative data of the deviation data of the corresponding points, includes: Count the frequency of the same deviation value occurring in the deviation direction according to the order of the magnitude of the deviation value; Based on the normal distribution of the statistical results, the deviation value that appears most frequently in different deviation directions is obtained as representative data of the deviation data of the corresponding points.

5. The optical remote sensing data registration method for microsatellites according to claim 1, characterized in that, After registering the registered band data to the reference band data according to the difference parameters, the optical remote sensing data registration method of the micro-nano satellite further includes: The registered band data and the reference band data are fused together to obtain a fused image.

6. The optical remote sensing data registration method for microsatellites according to claim 1, characterized in that, The acquisition of the corresponding point deviation data between the registration band data and the reference band data includes: Obtain the pixel deviation data of the corresponding points between the registration band data and the reference band data; The step of statistically analyzing the distribution of the corresponding point deviation data and obtaining representative data of the corresponding point deviation data includes: The distribution of the pixel deviation data of the corresponding points is statistically analyzed to obtain representative data of the pixel deviation data of the corresponding points.

7. The optical remote sensing data registration method for microsatellites according to claim 1, characterized in that, Before acquiring the corresponding point deviation data between the registration band data and the reference band data, the optical remote sensing data registration method of the micro-nano satellite further includes: Acquire several band data of the spectral data; wherein the several band data include red band data, green band data and blue band data.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the optical remote sensing data registration method for microsatellites as described in any one of claims 1-7.

9. An electronic device, characterized in that, Including processor and memory, among which, The memory is used to store computer programs; The processor is used to load and execute the computer program to enable the electronic device to perform the optical remote sensing data registration method for microsatellites as described in any one of claims 1-7.

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