Self-Supervised Stereo Matching Method Based on SRWP and PCAM for Urban Satellite Images

Abstract

In this paper, we propose a self-supervised stereo matching method based on superpixel random walk pre-matching (SRWP) and parallax-channel attention mechanism (PCAM). Our method is divided into two stages, training and testing. First, in the training stage, we obtain pre-matching results of stereo images based on superpixel random walk, and some matching points with high confidence are selected as labeled samples. Then, a stereo matching network is constructed to describe the matching correlation by calculating the attention scores of any two points between different images through the parallax-channel attention mechanism, superimposing the scores of each layer to calculate the disparity. The network is trained using the labeled samples and some unsupervised constraint criteria. Finally, in the testing stage, the trained network is used to obtain stereo matching relations of stereo images. The proposed method does not need manually labeled training samples and is more suitable for 3D reconstruction under mass satellite remote sensing data. Comparative experiments on multiple datasets show that our method has a stereo matching EPE of 2.44 and a 3D reconstruction RMSE of 2.36 m. Especially in the weak texture and parallax abrupt change regions, we can achieve more advanced performance than other methods.