Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation

作者：Yang Zhao, Peng Guo, Han Gao, Xiuwan Chen · 发表于：IEEE Geoscience and Remote Sensing Letters · 年份：2023 · DOI：10.1109/lgrs.2022.3233644 · 被引用次数：14 · 研究领域：Advanced Neural Network Applications、Domain Adaptation and Few-Shot Learning、Multimodal Machine Learning Applications

Unsupervised domain adaptation (UDA) is an approach to minimizing the domain gap. Generative methods are common approaches to minimizing the domain gap of aerial images, which improves the performance of the downstream tasks, for example, cross-domain semantic segmentation. For aerial images, the digital surface model (DSM) is usually available in both the source domain and the target domain. Depth information in DSM brings external information to generative models. However, little research utilizes it. In this letter, depth-assisted ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL) and depth cycle consistency loss (DCCL) are used to bring depth information into the generative model. Experimental results show that DRDG reaches state-of-the-art accuracy between generative methods in cross-domain semantic segmentation tasks. Source code is available athttps://github.com/miemieyanga/ResiDualGAN-DRDG.