Adaptive Context Transformer for Semisupervised Remote Sensing Image Segmentation

Current deep learning methods for semantic segmentation in remote sensing heavily depend on a substantial amount of labeled data. However, obtaining pixel-level labeled data in this field is both time-consuming and laborious. To address this challenge, semi-supervised learning methods have been introduced. Pseudo supervision is one of the most effective methods, which can be adopted to enhance the performance of semi-supervised semantic segmentation of remote sensing images [1]. But incorrect pseudo labels can cause substantially distortions to the segmentation model in semi-supervised learning. Moreover, it is difficult for conventional semantic segmentation methods to deal with global-local features of the remote sensing image without adaptive context feature. In this paper, we propose a novel learning approach based on an adaptive context transformer and pseudo labeling, called Adaptive Context Transformer for semi-supervised (ACTSS) remote sensing image segmentation. We propose an adaptive context attention model with adjustable sliding windows. A small window is used to capture Query (Q) for local feature and bigger windows are used to capture Key (K) and Value (V) for global feature. Then we combine them and get the global-local feature. And we propose a point-line-plane pseudo label filter (PLP) mechanism based on clustering and boundary extraction, which can filter unreliable pseudo labels from three angles: point, line and plane. To validate the effectiveness of the model, we carried out extensive experiments on the LOVEDA, Potsdam and Vaihingen datasets, and compared ACTSS with other methods. These experiments demonstrate that ACTSS achieves state-of-the-art performance for semi-supervised semantic segmentation on all tested datasets.