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Recently, Unsupervised Domain Adaptation (UDA) has attracted increasingattention to address the domain shift problem in the semantic segmentationtask. Although previous UDA methods have achieved promising performance, theystill suffer from the distribution gaps between source and target domains,especially the resolution discrepany in the remote sensing images. To addressthis problem, this paper designs a novel end-to-end semantic segmentationnetwork, namely Super-Resolution Domain Adaptation Network (SRDA-Net). SRDA-Netcan simultaneously achieve the super-resolution task and the domain adaptationtask, thus satisfying the requirement of semantic segmentation for remotesensing images which usually involve various resolution images. The proposedSRDA-Net includes three parts: a Super-Resolution and Segmentation (SRS) modelwhich focuses on recovering high-resolution image and predicting segmentationmap, a Pixel-level Domain Classifier (PDC) for determining which domain thepixel belongs to, and an Output-space Domain Classifier (ODC) fordistinguishing which domain the pixel contribution is from. By jointlyoptimizing SRS with two classifiers, the proposed method can not onlyeliminates the resolution difference between source and target domains, butalso improve the performance of the semantic segmentation task. Experimentalresults on two remote sensing datasets with different resolutions demonstratethat SRDA-Net performs favorably against some state-of-the-art methods in termsof accuracy and visual quality. Code and models are available athttps://github.com/tangzhenjie/SRDA-Net.

Super-Resolution Domain Adaptation Networks for Semantic Segmentation via Pixel and Output Level Aligning