ARSRNet: accurate space object recognition using optical cross section curves.

Abstract

Limited by the conditions and performance of ground-based optical observations, it is difficult for us to obtain a plethora of optical cross section (OCS) data for some space objects (SOs). Unevenly distributed OCS data and unclear labels will affect the performance of SOs recognition based on neural networks. Furthermore, when we need to identify a new SO or SO category using deep neural network, the trained network model may no longer be applicable. We need to retrain the network with new training data. In order to alleviate these problems and improve the generalization and training convergence speed of SOs recognition networks, a novel, to the best of our knowledge, neural network model, ARSRNet, is proposed in this paper. The ARSRNet can identify SOs and their attitude accurately using only a small quantity of training OCS data and without clear labels. And the proposed network is able to adapt to new recognition tasks. Meanwhile, we propose an AdamRprop network optimization algorithm to accelerate network training and improve recognition accuracy. Experimental results show that the recognition accuracy of ARSRNet reaches 90.60% on the test OCS dataset. Compared with mainstream network optimization algorithms, the proposed AdamRprop is more appropriate for ARSRNet and can accelerate the convergence of ARSRNet.