IterDANet: Iterative Intra-Domain Adaptation for Semantic Segmentation of Remote Sensing Images
Yuxiang Cai, Yingchun Yang, Yongheng Shang, Zhenqian Chen, Zhengwei Shen, Jianwei Yin
Abstract
When segmenting the continuous proliferation of unlabeled remotely sensed images, unsupervised domain adaptation (UDA) has become one of the most critical techniques and achieved significant performance. But in fact, there still exists a large performance gap between the existing UDA frameworks and supervised learning methods, for the majority of UDA frameworks don’t consider the intra-domain gap in the target domain. In this paper, to further minimize the complex intra-domain shift within the target domain in remote sensing, we propose a novel iterative intra-domain adaptation framework (IterDANet), which conducts inter-domain adaptation (InterDA), entropy-based ranking (ER) and iterative intra-domain adaptation (IntraDA). Specifically, first, to enhance the performance of InterDA built upon GAN-based image-to-image translation, we propose a new generator selection strategy to assess and choose a well-trained generator for the inter-domain classifier. Then, to produce more accurate pseudo labels for IntraDA, we propose a new pseudo label generation strategy to remove both high-entropy and low-confident pixels in predicted maps of inter-domain classifier. Finally, to better reduce the intra-domain gap, we propose to cluster all the target images into multiple subdomains using ER and iteratively align the cleanest subdomain with other noisy subdomains. The extensive experiments on the benchmark dataset, which includes cross-city aerial images, highlight the superiority and effectiveness of our IterDANet against the state-of-the-art UDA frameworks.