Active Domain Adaptive Semantic Segmentation with Regional Relative Entropy for Remote Sensing Images
Fei Liang, Ruizhi Yang, Zhengyao Wang, Yang Hu, Dong Zhao, Shuang Wang · 2024
This paper presents a novel approach using active learning to tackle domain adaptation challenges in remote sensing semantic segmentation. Unsupervised Domain Adaptation for Semantic Segmentation (UDASS) aims to transfer a model trained on labeled source domain data to an unlabeled target domain. Existing UDASS methods struggle with the complexity of domain shift factors in remote sensing scenes, such as resolution, imaging mechanisms, geography, and species distribution, falling short of fully supervised performance. To address this, we propose integrating active learning, selecting a valuable (e.g. 2.2%) subset of pixel annotations from the target domain, and combining it with UDASS methods. Our method devises region-relative entropy metric to identify informative yet challenging pixels, facilitating better adaptation. Experimental results on two challenging domain adaptation tasks validate the efficacy of our technique, achieving performance comparable to fully supervised pixel labeling with only 2.2% annotated data.