A Large-Scale Invariant Matching Method Based on DeepSpace-ScaleNet for Small Celestial Body Exploration

Abstract

Small Celestial Body (SCB) image matching is essential for deep space exploration missions. In this paper, a large-scale invariant method is proposed to improve the matching accuracy of SCB images under large-scale variations. Specifically, we designed a novel network named DeepSpace-ScaleNet, which employs an attention mechanism for estimating the scale ratio to overcome the significant variation between two images. Firstly, the Global Attention-DenseASPP (GA-DenseASPP) module is proposed to refine feature extraction in deep space backgrounds. Secondly, the Correlation-Aware Distribution Predictor (CADP) module is built to capture the connections between correlation maps and improve the accuracy of the scale distribution estimation. To the best of our knowledge, this is the first work to explore large-scale SCB image matching using Transformer-based neural networks rather than traditional handcrafted feature descriptors. We also analysed the effects of different scale and illumination changes on SCB image matching in the experiment. To train the network and verify its effectiveness, we created a simulation dataset containing light variations and scale variations named Virtual SCB Dataset. Experimental results show that the DeepSpace-ScaleNet achieves a current state-of-the-art SCB image scale estimation performance. It also shows the best accuracy and robustness in image matching and relative pose estimation.