The growing number of satellites in orbit has resulted in a rise in defunct satellites and space debris, posing a significant risk to valuable spacecraft like normal satellites and space stations. Therefore, the removal of defunct satellites and space debris has become increasingly crucial. This article presents a segmentation method for satellite images captured in the visible light spectrum in space. Firstly, due to the lack of real space satellite images, we used optical simulation and Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation (U-GAT-IT) to generate realistic space satellite images in the visible light spectrum and constructed a dataset. Secondly, we proposed an Attention Supervision Transformer Full-Resolution Residual Network (ASTransFRRN), which integrates transformer, attention mechanism and deep supervision, to segment satellite bodies, solar panels, and the cosmic background. Finally, we evaluated the proposed method using the U-GAT-IT simulated dataset and compared its performance with state-of-the-art methods. The proposed method achieved a segmentation accuracy of 90.77%±7.04% for satellite bodies, 90.61%±16.48% for satellite solar panels, and 97.66%±1.94% for the cosmic background. The overall pixel segmentation accuracy was 97.22%±2.78%, outperforming the compared methods in terms of segmentation accuracy. The proposed ASTransFRRN demonstrated a significant improvement in the segmentation accuracy of the main components of space satellites.
This paper proposes a new method for detecting small infrared targets, which addresses the issue of low detection probability (DP) and high false alarm probability (FAP) caused by false alarm sources such as high bright background edge or independent noise. The method employs a three-layer window for local contrast calculation to obtain a more accurate reference value of the background, which can enhance real targets and suppress complex backgrounds. It also solves the problems of multi-scale target detection and independent noise removal by using rank order filtering of fixed center window. Furthermore, targets are enhanced using the gray scale distributions of their edges contrast calculation, thereby improving the DP and reducing the FAP. Experimental validation on several infrared sequences and images confirms the effectiveness and robustness of the proposed method, which outperforms five existing algorithms in terms of DP and FAP.
The camera mounted on the satellite two-dimensional tracking platform takes images of the star background, and using the star map recognition method based on triangle matching can eliminate stars and extract non-stellar targets. When the tracking platform is used to track the non-stellar targets and is moving fast, the tracking targets will be lost. To solve this problem, a spatial target tracking method based on stellar background is proposed in this paper. Firstly, the rotation matrix of the camera in the geocentric equatorial inertial coordinate system at the moment of two consecutive image frames is calculated based on triangle matching, and targets of each image frame are extracted. For a certain target, the coordinate position corresponding to the target are calculated based on the transformation of the rotation matrix. And then the motion of the target in the inertial coordinate system is superimposed to derive the predicted coordinate position of the target. Finally, the nearest Euclidean distance from the predicted coordinates is found among the current target points. If the Euclidean distance is less than a certain threshold value, the target tracking is successful. The simulation experiment shows that when the tracking platform is moving fast, the target can be tracked continuously and stably.
In order to improve image processing quality and boost processing rate, this paper proposes an real-time automatic
image enhancement algorithm. It is based on the histogram equalization algorithm and the piecewise linear enhancement
algorithm, and it calculate the relationship of the histogram and the piecewise linear function by analyzing the histogram
distribution for adaptive image enhancement. Furthermore, the corresponding FPGA processing modules are designed to
implement the methods. Especially, the high-performance parallel pipelined technology and inner potential parallel
processing ability of the modules are paid more attention to ensure the real-time processing ability of the complete
system. The simulations and the experimentations show that the algorithm is based on the design and implementation of
FPGA hardware circuit less cost on hardware, high real-time performance, the good processing performance in different
sceneries. The algorithm can effectively improve the image quality, and would have wide prospect on imaging
processing field.
In this paper, the hardware friendly adaptive support-weight approach is proposed to simplify the weight calculation process of the standard approach, which employs the support region to simplify the calculation of the similarity and uses the fixed distance dependent weight to present the proximity. In addition, the complete stereo matching algorithm and the hardware structure for FPGA implementation compatible with the approach is proposed. The experimental results show that the algorithm produces the disparity map accurately in different illumination conditions and different scenes, and its processing average bad pixel rate is only 6.65% for the standard test images of the Middlebury database, which is approximate to the performance of the standard adaptive support-weight approach. The proposed hardware structure provides a basis for design and implementation of real-time accurate stereo matching FPGA system.
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