Unsupervised Domain Adaptation for Semantic Segmentation of High-Resolution Remote Sensing Imagery Driven by Category-Certainty Attention

Semantic segmentation is an important task of analysis and understanding of high-resolution remote sensing images (HRSIs). The deep convolutional neural network (DCNN)-based model shows their excellent performance in remote sensing image semantic segmentation. Most of the existing HRSI semantic segmentation methods are only designed for a very limited data domain, that is, the training and test images are from the same dataset. The accuracy drops sharply once a model trained on a certain dataset is used for cross-domain prediction due to the difference in feature distribution of the dataset. To this end, this article proposes an unsupervised domain adaptation framework based on adversarial learning for HRSI semantic segmentation. This framework uses high-level feature alignment to narrow the difference between the source and target domains at the semantic level. It uses the category-certainty attention module to reduce the attention of the classifier on category-level aligned features and increase the attention on category-level unaligned features. Experimental results show that the proposed method performs favorably against the state-of-the-art methods in cross-domain segmentation.