A remote sensing micro-nano satellite integrated information processing platform

Abstract

The application discloses a kind of remote sensing micro-nano satellite integrated information processing platform, belong to aerospace field, including satellite optical load module, data processing module, image correction module;The satellite optical load module is used to provide load image data to the data processing module;The data processing module is divided into several frame pictures according to preset time period with load image data, then several frame pictures are transferred to image correction module;The image correction module receives several frame pictures and detects, judges whether there is deformation, if there is, it is adjusted, then the picture after adjustment is sent to data processing module;If not, the original picture is sent back to data processing module.The application, by the image correction module set, can delete invalid picture, and then avoid affecting correction accuracy, so as to effectively improve the effect of image detection correction.

Description

Technical Field

[0001] This invention relates to the field of aerospace technology, specifically to an integrated information processing platform for remote sensing micro-nano satellites. Background Technology

[0002] Driven by the development of advanced technologies and the demands of the market, remote sensing micro- and nano-satellites, with their advantages of light weight, small size, low power consumption, short development cycle, high functional density, high performance-price ratio, and ability to be networked, have shown promising prospects in scientific research, defense, and commercial fields such as resource management, environmental monitoring, land planning, and geographic mapping. However, because remote sensing micro- and nano-satellite platforms often employ independent designs for data processing and payload data processing, and because there is actually a certain error between the satellite attitude data and the actual attitude of the optical payload, this has a significant impact on the payload's imaging quality.

[0003] Currently, remote sensing micro-nano satellite data processing platforms mostly adopt a distributed design, meaning that micro-nano satellites often use the traditional satellite platform method to process data and payload data independently.

[0004] However, existing technologies have the following drawbacks: the image detection and correction effects are not significant, and the failure to remove invalid images can easily affect the correction accuracy. Therefore, those skilled in the art provide an integrated information processing platform for remote sensing micro-nano satellites to solve the problems mentioned in the background. Summary of the Invention

[0005] The purpose of this invention is to provide an integrated information processing platform for remote sensing micro-nano satellites. By setting up an image correction module, invalid images can be deleted, thereby avoiding affecting the correction accuracy and effectively improving the image detection and correction effect, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An integrated information processing platform for remote sensing micro-nano satellites includes a satellite optical payload module, a data processing module, and an image correction module;

[0008] The satellite optical payload module is used to provide payload image data to the data processing module;

[0009] The data processing module divides the payload image data into several frames according to a preset time period, and then transmits the several frames to the image correction module.

[0010] The image correction module receives several frames of images for detection to determine whether there is distortion. If distortion exists, it adjusts the images and sends the adjusted images to the data processing module. If distortion does not exist, the original images are sent back to the data processing module.

[0011] The specific detection process of the image correction module is as follows:

[0012] S1: Label several frames of images as Ti, i = 1...n, according to the timeline;

[0013] S2: Divide each frame image Ti into a nine-grid image for detection, remove invalid images and rearrange them;

[0014] S3: Perform contour comparison between each frame image Ti and the two frames Ti-1 and Ti+1 on the left and right.

[0015] The image correction module can delete invalid images, thereby avoiding affecting the correction accuracy and effectively improving the image detection and correction effect.

[0016] As a further aspect of the present invention: in S2, the specific process of rearranging is as follows:

[0017] S201: Divide each frame image Ti into a nine-grid image, and label them sequentially as Tij, j = 1, 2, 3...9;

[0018] S201: Perform edge feature recognition on any image Tij and its adjacent images;

[0019] S201: Take the feature label of the edge Tij in the image as Zi, i = 1...n;

[0020] S201: Align and stitch the Zi of one image with the Zi of the adjacent images;

[0021] S201: Count the number of irregular features and mark them as K. If K reaches the preset value, remove the image Ti and rearrange it.

[0022] This setting effectively removes distorted images, thus facilitating subsequent similarity detection and improving its accuracy.

[0023] As a further aspect of the present invention: in S3, the specific process of contour comparison is as follows:

[0024] S301: Convert several rearranged frames of images into grayscale images;

[0025] S302: Scan grayscale, extract gradient, and form contour;

[0026] S303: Analyze the similarity between the outline of the contour in step S301 and the contours of the left and right images;

[0027] S304: If the similarity is lower than the preset value, adjust the transformed graphic according to the similarity, and then perform similarity analysis again between the outline of the graphic and the outlines of the left and right images until the similarity reaches the preset value.

[0028] S305: If the similarity reaches the preset value, the adjustment ends;

[0029] S306: Send the adjusted image back to the data processing module.

[0030] This setting can effectively correct the images, thereby improving the quality of the images generated by the remote sensing micro-nano satellite.

[0031] As a further aspect of the present invention: in S303, the specific analysis process is as follows:

[0032] (1): Identify each feature point in image Ti and images Ti-1 and Ti+1, and label them as Pi, i = 1... n;

[0033] (2): Overlay the grayscale images Ti with Ti-1 and Ti+1;

[0034] (3): Match and compare the grayscale images of image Ti with those of images Ti-1 and Ti+1, and display them over each other;

[0035] (4): Identify feature points that do not overlap with each other in the grayscale images of image Ti and images Ti-1 and Ti+1;

[0036] (5): Establish a detection model, establish coordinate axes with the overlapping image surfaces, import the positions of the inconsistent feature points on the coordinate axes into the model, and determine whether each feature point constitutes a linear relationship. If so, output the similarity is qualified; otherwise, proceed to the next step.

[0037] (6): Count the number of the above inconsistent feature points and label them as mi, i = 1...n;

[0038] (7): The similarity of the markers is V, where V = 1 - mi * 0.01;

[0039] (8): Output similarity V.

[0040] This setting effectively distinguishes the similarity between images, thus facilitating correction and adjustment.

[0041] As a further aspect of the present invention: the remote sensing micro-nano satellite integrated information processing platform further includes a data storage module, which stores the data of the remote sensing micro-nano satellite integrated information processing platform through a disk array. The data storage module is interconnected with the data processing module through a high-speed serial bus with a transmission speed of 6Gbps. The data storage module is used to store the payload image data with a storage speed of 10Gbps and a storage capacity of 3TB.

[0042] The data storage module is designed to facilitate data saving and retrieval when needed later.

[0043] As a further aspect of the present invention: the integrated information processing platform for remote sensing micro-nano satellites also includes a power supply module, which is used to convert the main power supply power of the remote sensing micro-nano satellite into auxiliary power supply power, and the auxiliary power supply provides power to the data processing module, the satellite optical payload module, the image correction module and the data storage module.

[0044] The power module acts as a backup power source, which can improve the operational stability of remote sensing micro- and nano-satellites.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] This invention, through its image correction module, can delete invalid images, thereby avoiding impact on correction accuracy and effectively improving the image detection and correction effect. When deleting invalid images, this invention first uses a nine-grid image segmentation method, then performs edge feature recognition and stitching, effectively identifying severely misaligned images, and then deleting these invalid images before rearranging them. Furthermore, the similarity analysis method of this invention is more reasonable than traditional methods, avoiding errors caused by the relative changes in the positions of microsatellites and nanosatellites relative to the Earth, and can quickly calculate similarity for easier analysis. Attached Figure Description

[0047] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Wherein:

[0048] Figure 1 This is a schematic diagram of the structure of an integrated information processing platform for remote sensing micro-nano satellites. Detailed Implementation

[0049] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0050] According to one embodiment of the present invention, Figure 1 As shown. A remote sensing micro-nano satellite integrated information processing platform includes a satellite optical payload module, a data processing module, and an image correction module;

[0051] The satellite optical payload module is used to provide payload image data to the data processing module;

[0052] The data processing module divides the payload image data into several frames according to a preset time period, and then transmits these frames to the image correction module.

[0053] The image correction module receives several frames of images for detection to determine whether there is any distortion. If distortion exists, it adjusts the image and sends the adjusted image to the data processing module; otherwise, the original image is sent back to the data processing module.

[0054] The specific detection process of the image correction module is as follows:

[0055] S1: Label several frames of images as Ti, i = 1...n, according to the timeline;

[0056] S2: Divide each frame image Ti into a nine-grid image for detection, remove invalid images and rearrange them;

[0057] S3: Perform contour comparison between each frame image Ti and the two frames Ti-1 and Ti+1 on the left and right.

[0058] The image correction module can delete invalid images, thereby avoiding affecting the correction accuracy and effectively improving the image detection and correction effect.

[0059] In this embodiment: the specific process of rearranging in S2 is as follows:

[0060] S201: Divide each frame image Ti into a nine-grid image, and label them in order as Tij, j = 1, 2, 3...9;

[0061] S201: Perform edge feature recognition on any image Tij and its adjacent images;

[0062] S201: Take the feature label of the edge Tij in the image as Zi, i = 1...n;

[0063] S201: Align and stitch the Zi of one image with the Zi of the adjacent images;

[0064] S201: Count the number of irregular features and mark them as K. If K reaches the preset value, remove the image Ti and rearrange it.

[0065] This setting effectively removes distorted images, thus facilitating subsequent similarity detection and improving its accuracy.

[0066] In this embodiment: In S3, the specific process of contour comparison is as follows:

[0067] S301: Convert several rearranged frames of images into grayscale images;

[0068] S302: Scan grayscale, extract gradient, and form contour;

[0069] S303: Analyze the similarity between the outline of the contour in step S301 and the contours of the left and right images;

[0070] S304: If the similarity is lower than the preset value, adjust the transformed graphic according to the similarity, and then perform similarity analysis again between the outline of the graphic and the outlines of the left and right images until the similarity reaches the preset value.

[0071] S305: If the similarity reaches the preset value, the adjustment ends;

[0072] S306: Send the adjusted image back to the data processing module.

[0073] This setting can effectively correct the images, thereby improving the quality of the images generated by the remote sensing micro-nano satellite.

[0074] In this embodiment: In S303, the specific analysis process is as follows:

[0075] (1): Identify each feature point in image Ti and images Ti-1 and Ti+1, and label them as Pi, i = 1... n;

[0076] (2): Overlay the grayscale images Ti with Ti-1 and Ti+1;

[0077] (3): Match and compare the grayscale images of image Ti with those of images Ti-1 and Ti+1, and display them over each other;

[0078] (4): Identify feature points that do not overlap with each other in the grayscale images of image Ti and images Ti-1 and Ti+1;

[0079] (5): Establish a detection model, establish coordinate axes with the overlapping image surfaces, import the positions of the inconsistent feature points on the coordinate axes into the model, and determine whether each feature point constitutes a linear relationship. If so, output the similarity is qualified; otherwise, proceed to the next step.

[0080] (6): Count the number of the above inconsistent feature points and label them as mi, i = 1...n;

[0081] (7): The similarity of the markers is V, where V = 1 - mi * 0.01;

[0082] (8): Output similarity V.

[0083] This setting effectively distinguishes the similarity between images, thus facilitating correction and adjustment.

[0084] In this embodiment, the integrated remote sensing micro-nano satellite information processing platform also includes a data storage module. This module stores data from the platform via a disk array and is interconnected with the data processing module via a high-speed serial bus with a transmission speed of 6Gbps. The data storage module stores payload image data at a speed of 10Gbps and has a storage capacity of 3TB. This data storage module facilitates data saving and retrieval when needed later.

[0085] In this embodiment, the integrated information processing platform for remote sensing micro-nano satellites also includes a power module. This power module converts the main power supply of the remote sensing micro-nano satellite into auxiliary power supply, which powers the data processing module, satellite optical payload module, image correction module, and data storage module. The power module acts as a backup power source, improving the operational stability of the remote sensing micro-nano satellite.

[0086] The working principle of this invention is as follows: When using this integrated remote sensing micro-nano satellite information processing platform, firstly, the satellite optical payload module provides payload image data to the data processing module; then, the data processing module divides the payload image data into several frames according to a preset time period, and then transmits these frames to the image correction module; the image correction module receives these frames and checks them to determine if distortion exists. If distortion exists, it adjusts the distortion and sends the adjusted image back to the data processing module; if no distortion exists, the original image is sent back to the data processing module. Specifically, the image correction module's detection process is as follows: several frames are labeled as Ti according to the time axis, i = 1...n; each frame Ti is then divided into a nine-grid image for detection, invalid images are removed, and the grid is rearranged; finally, each frame Ti is compared with the outlines of its two adjacent frames Ti-1 and Ti+1. Furthermore, the specific process of contour comparison is as follows: convert several rearranged frames of images into grayscale images; then scan the grayscale, extract the gradient, and form a contour; then analyze the similarity between the selected contour outline and the contours of the left and right images; if the similarity is lower than a preset value, adjust the transformed graphic according to the similarity, and then perform similarity analysis between the graphic contour outline and the contours of the left and right images again until the similarity reaches the preset value; then, if the similarity reaches the preset value, the adjustment ends; finally, send the adjusted image back to the data processing module.

[0087] This invention, through its image correction module, can delete invalid images, thereby avoiding impact on correction accuracy and effectively improving the image detection and correction effect. When deleting invalid images, this invention first uses a nine-grid image segmentation method, then performs edge feature recognition and stitching, effectively identifying severely misaligned images, and then deleting these invalid images before rearranging them. Furthermore, the similarity analysis method of this invention is more reasonable than traditional methods, avoiding errors caused by the relative changes in the positions of microsatellites and nanosatellites relative to the Earth, and can quickly calculate similarity for easier analysis.

[0088] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A remote sensing micro / nano satellite integrated information processing platform, characterized in that, Includes satellite optical payload module, data processing module, and image correction module; The satellite optical payload module is used to provide payload image data to the data processing module; The data processing module divides the payload image data into several frames according to a preset time period, and then transmits the several frames to the image correction module. The image correction module receives several frames of images for detection to determine whether there is distortion. If distortion exists, it adjusts the images and sends the adjusted images to the data processing module. If distortion does not exist, the original images are sent back to the data processing module. The specific detection process of the image correction module is as follows: S1: Label several frames of images as Ti, i=1……n, according to the timeline; S2: Divide each frame image Ti into a nine-grid image for detection, remove invalid images and rearrange them; S3: Perform contour comparison between each frame image Ti and the two frames Ti-1 and Ti+1 on the left and right; In S2, the specific process of rearranging is as follows: S201: Divide each frame image Ti into a nine-grid image, and label them in order as Tij, j=1, 2, 3...9; S201: Perform edge feature recognition on any image Tij and its adjacent images; S201: Take the feature label of the edge Tij in the image as Zi, i=1……n; S201: Align and stitch the Zi of one image with the Zi of the adjacent images; S201: Count the number of irregular features and mark them as K. If K reaches the preset value, remove the image Ti and rearrange it. In S3, the specific process of contour comparison is as follows: S301: Convert several rearranged frames of images into grayscale images; S302: Scan grayscale, extract gradient, and form contour; S303: Analyze the similarity between the outline of the contour in step S301 and the contours of the left and right images; S304: If the similarity is lower than the preset value, adjust the transformed graphic according to the similarity, and then perform similarity analysis again between the outline of the graphic and the outlines of the left and right images until the similarity reaches the preset value. S305: If the similarity reaches the preset value, the adjustment ends; S306: Send the adjusted image back to the data processing module; In S303, the specific analysis process is as follows: (1): Identify each feature point in image Ti and images Ti-1 and Ti+1, and label them as Pi, i=1……n; (2): Overlay the grayscale images Ti with those of Ti-1 and Ti+1; (3): Match and compare the grayscale images of image Ti with those of images Ti-1 and Ti+1, and display them over each other; (4): Identify feature points that do not overlap with each other in the grayscale images of image Ti and images Ti-1 and Ti+1; (5): Establish a detection model, establish coordinate axes with the overlapping image surfaces, import the positions of the inconsistent feature points on the coordinate axes into the model, and determine whether each feature point constitutes a linear relationship. If so, output the similarity is qualified; otherwise, proceed to the next step. (6): Count the number of the above inconsistent feature points and label them as mi, i=1……n; (7): The similarity of the markers is V, where V = 1 - mi * 0.01; (8): Output similarity V.

2. The remote sensing micro-nano satellite integrated information processing platform according to claim 1, characterized in that, The integrated remote sensing micro-nano satellite information processing platform also includes a data storage module. The data storage module stores the data of the integrated remote sensing micro-nano satellite information processing platform through a disk array. The data storage module is interconnected with the data processing module through a high-speed serial bus with a transmission speed of 6Gbps. The data storage module is used to store the payload image data with a storage speed of 10Gbps and a storage capacity of 3TB.

3. The integrated information processing platform for remote sensing micro-nano satellites according to claim 1, characterized in that, The integrated information processing platform for remote sensing micro-nano satellites also includes a power module, which is used to convert the main power supply of the remote sensing micro-nano satellite into auxiliary power supply. The auxiliary power supply provides power to the data processing module, the satellite optical payload module, the image correction module, and the data storage module.

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