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

• Published in: arXiv.org
• Main Authors: Wu, Junfeng, Tang, Zhenjie, Xu, Congan, Liu, Enhai, Long, Gao, Yan, Wenjun
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 13.05.2022
• Subjects: Adaptation; Artificial neural networks; Classifiers; Computer Science - Computer Vision and Pattern Recognition; Discriminators; Domains; Image resolution; Image segmentation; Machine learning; Pixels; Remote sensing; Semantic segmentation; Semantics
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2005.06382

Recently, Unsupervised Domain Adaptation (UDA) has attracted increasing attention to address the domain shift problem in the semantic segmentation task. Although previous UDA methods have achieved promising performance, they still suffer from the distribution gaps between source and target domains, especially the resolution discrepany in the remote sensing images. To address this problem, this paper designs a novel end-to-end semantic segmentation network, namely Super-Resolution Domain Adaptation Network (SRDA-Net). SRDA-Net can simultaneously achieve the super-resolution task and the domain adaptation task, thus satisfying the requirement of semantic segmentation for remote sensing images which usually involve various resolution images. The proposed SRDA-Net includes three parts: a Super-Resolution and Segmentation (SRS) model which focuses on recovering high-resolution image and predicting segmentation map, a Pixel-level Domain Classifier (PDC) for determining which domain the pixel belongs to, and an Output-space Domain Classifier (ODC) for distinguishing which domain the pixel contribution is from. By jointly optimizing SRS with two classifiers, the proposed method can not only eliminates the resolution difference between source and target domains, but also improve the performance of the semantic segmentation task. Experimental results on two remote sensing datasets with different resolutions demonstrate that SRDA-Net performs favorably against some state-of-the-art methods in terms of accuracy and visual quality. Code and models are available at https://github.com/tangzhenjie/SRDA-Net.