MASDG: Multiview Augmented Single-Source Domain Generalization Method for Robust Remote Sensing Building Extraction

作者：Yunjiao Liu, Yuanyuan Liu, Kejun Liu, Yuxuan Huang, Chang Tang, Wujie Zhou, Zhe Chen, Wei Xiang, Hongyan Zhang · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2025 · DOI：10.1109/tgrs.2025.3640112 · 被引用次数：3 · 研究领域：Domain Adaptation and Few-Shot Learning、Advanced Neural Network Applications、Remote-Sensing Image Classification

Despite advances in deep learning for remote sensing building extraction (RSBE), Multi-target Domain RSBE (MD-RSBE) remains challenging, as it requires transferring knowledge from a labeled source domain to multiple unlabeled target domains, with domain shifts in texture, style, and semantics. Existing domain adaptation (DA) and generalization (DG) methods face significant limitations: DA requires target-domain training, while DG needs multi-source training, leading to high training costs and low generalization in practical MD-RSBE scenarios. To address this, we propose a Multi-view Augmented Single-source Domain Generalization (MASDG) method, which effectively mitigates domain shifts across RS source and target domains for robust MD-RSBE performance by enriching the diversity of the source domain through multi-view augmentation and enforcing semantic consistency. Specifically, MASDG consists of three key components: Texture-level Domain Augmentation (TDA) module, Style-level Domain Augmentation (SDA) module and Semantic-invariant Representation Learning (SRL). To mitigate texture-level domain shift, TDA first introduces parameter-optimized multi-layer random convolution to modify the texture of source image, generating texture-augmented image pairs for simulating real-world texture diversity across various RS domains. Then, with each image pair from TDA, SDA employs two paralleled encoders, namely the general feature encoder and the batch-guided style encoder, to formulate m...