Unsupervised Domain Adaptation Semantic Segmentation for Remote-Sensing Images via Covariance Attention

Semantic segmentation for remote sensing is a crucial but challenging task. Many supervised semantic segmentation methods rely heavily on a large-scale pixelwise annotated dataset, but it is time-consuming and laborious to provide manual annotation. However, due to the common domain shift of remote-sensing images, a direct transfer might not perform well. Therefore, many unsupervised domain adaptation (UDA) methods have been proposed to solve the data distribution discrepancy in remote-sensing datasets, but these methods cannot completely utilize the features extracted in the training process. In addition, the correlations between feature map channels are crucial for the pixelwise classification task. In this letter, a covariance-based channel attention module is proposed to capture correlations by covariance metric and weighting the feature map channels. To further improve the domain adaptation performance, we propose a three-stage UDA semantic segmentation method for remote-sensing images, and we fine-tune the model that has been trained on the source domain on the target domain via self-training and knowledge distillation (KD). To test the effectiveness of the proposed method, experiments are conducted on the ISPRS 2-D Semantic Labeling dataset and an urban drone dataset (UDD). Our method shows a better performance advantage compared with other state-of-the-art methods.