An End-to-End Identification Algorithm for Smearing Star Image

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 22; p. 4541
• Main Authors: Han, Jinliang, Yang, Xiubin, Xu, Tingting, Fu, Zongqiang, Chang, Lin, Yang, Chunlei, Jin, Guang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: Algorithms; Angular velocity; Artificial neural networks; Coders; Datasets; end-to-end identification; Energy; Feature extraction; Identification; Image classification; Methods; Neural networks; Noise; Satellites; Semantics; Sensors; Simulation; smearing image; star identification; Three axis; transformer encoder; Velocity
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13224541

In the previous study, there were a few direct star identification (star-ID) algorithms for smearing star image. An end-to-end star-ID algorithm is proposed in this article, to directly identify the smearing image from star sensors with fast attitude maneuvering. Combined with convolutional neural networks and the self-attention mechanism of transformer encoder, the algorithm can effectively classify the smearing image and identify the star. Through feature extraction and position encoding, neural networks learn the position of stars to generate semantic information and realize the end-to-end identification for the smearing star image. The algorithm can also solve the problem of low identification rate due to smearing of long exposure time for images. A dataset of dynamic stars is analyzed and constructed based on multiple angular velocities. Experiment results show that, compared with representative algorithms, the identification rate of the proposed algorithm is improved at high angular velocities. When the three-axis angular velocity is 10°/s, the rate is still 60.4%. At the same time, the proposed algorithm has good robustness to position noise and magnitude noise.