A Space Non-Cooperative Target Recognition Method for Multi-Satellite Cooperative Observation Systems

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 18; p. 3368
• Main Authors: Zhang, Yue, Wang, Jianyuan, Chen, Jinbao, Shi, Donghao, Chen, Xiaotong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Accuracy; Aerospace environments; Algorithms; Analysis; Artificial satellites; Classification; cooperative observation; Data augmentation; Datasets; Debris; Deep learning; foreground extraction; Identification methods; Image enhancement; lightweight model; Localization; Methods; non-cooperative target recognition; Orbital servicing; Registration; Remote sensing; Satellite imagery; Satellite observation; Satellites; Space debris; Space surveillance; Spacecraft; Stitching; Surveillance; Target detection; Target recognition
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16183368

Space non-cooperative target recognition is crucial for on-orbit servicing. Multi-satellite cooperation has great potential for broadening the observation scope and enhancing identification efficiency. However, there is currently a lack of research on recognition methods tailored for multi-satellite cooperative observation. In this paper, we propose a novel space non-cooperative target recognition method to identify satellites and debris in images from multi-satellite observations. Firstly, we design an image-stitching algorithm to generate space-wide-area images. Secondly, we propose a two-stage multi-target detection model, a lighter CNN model with distance merge threshold (LCNN-DMT). Specifically, in the first stage, we propose a novel foreground extraction model based on a minimum bounding rectangle with the threshold for distance merging (MBRT-D) to address redundant detection box extraction for satellite components. Then, in the second stage, we propose an improved SqueezeNet model by introducing separable convolution and attention mechanisms for target classification. Moreover, due to the absence of a public multi-target detection dataset containing satellites and debris, we construct two space datasets by introducing a randomized data augmentation strategy. Further experiments demonstrate that our method can achieve high-precision image stitching and superior recognition performance. Our LCNN-DMT model outperforms mainstream algorithms in target localization accuracy with only 0.928 M parameters and 0.464 GFLOPs, making it ideal for on-orbit deployment.