Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation Using Region and Category Adaptive Domain Discriminator

By reason of factors such as terrains, weather conditions, sensor imaging methods, and cultural and economic development, there is a large shift between the remote sensing imagery collected from different geographic locations and different sensors, which makes the state-of-the-art semantic segmentation models trained on source domain (an image set gathered from specific geographic locations and sensors) difficult to generalize to target domain (another image set collected from other geographic locations and sensors). Currently, unsupervised domain adaptation (UDA) using adversarial training, whose purpose is to align the marginal distribution in the output space between the source and target domains, is the most explored and practical approach to address this issue. However, this global alignment approach does not take into account diversities of different regions in a specific image nor the category-level distribution, which leads to the consequence that some regions and categories which are already well aligned between the source and target domains may be incorrectly remapped. Therefore, we propose a region and category adaptive domain discriminator (RCA-DD), aiming to emphasize the differences in regions and categories during the process of alignment. Specifically, on the one hand, we propose an entropy-based regional attention module (ERAM) in domain discriminator to emphasize the importance of difficult-to-align regions. On the other hand, we propose a class-clear module (CCM) to update only the distribution of existing categories in one iteration without affecting all categories. Finally, a lot of experiments are introduced to indicate that the proposed method can obtain better results when compared with other state-of-the-art UDA methods using adversarial training.