Uncertain Example Mining Network for Domain Adaptive Segmentation of Remote Sensing Images

Domain adaptive segmentation has recently gained more and more attention in the remote sensing field. However, current methods often generate a significant number of uncertain examples, i.e., noisy pseudo-labels, in the target domain, which adversely affects model convergence. To solve this issue, an uncertain example mining network is proposed for domain adaptive segmentation of remote sensing images. Specifically, a novel strategy called multilevel pseudo-label correcting (MPC) is proposed to correct the pseudo-labels in class, pixel, and superpixel levels. In this way, more reliable pseudo-labels can be selected for the subsequent training stage. Furthermore, a noise-robust example mining strategy, termed uncertainty-based valuable example mining (UVEM), is proposed to prioritize confident examples with significant gradients for training effectively. Extensive empirical evaluations on IsprsDA and LoveDA datasets demonstrate that the proposed method outperforms previous approaches, establishing state-of-the-art results in domain adaptive remote sensing image segmentation (RSIS). The code will be available at https://github.com/StuLiu/UemDA.