Feature disentanglement based domain adaptation network for cross-scene coastal wetland hyperspectral image classification

作者：Ziqi Xin, Zhongwei Li, Mingming Xu, Leiquan Wang, Guangbo Ren, Jianbu Wang, Yabin Hu · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2024 · DOI：10.1016/j.jag.2024.103850 · 被引用次数：20 · 研究领域：Remote-Sensing Image Classification、Domain Adaptation and Few-Shot Learning、Coral and Marine Ecosystems Studies

At present, domain adaptation (DA) methods have made noteworthy advancements in cross-scene hyperspectral image (HSI) classification. Their success largely hinges on the alignment of distributions between source and target domains, which is a critical step in extracting domain-invariant features. However, this intense focus on domain-invariant feature extraction frequently leads to the neglect of class-discriminative features, limiting their utility in cross-scene coastal wetland classification, where the nuanced identification of different classes is crucial. In this paper, a feature disentanglement based domain adaptation network (FDDAN) is proposed to disentangle and exclude domain-specific features and class-invariant features, thereby obtaining class-specific domain-invariant features for classification tasks. Specifically, a transformer and convolution fusion-based feature extraction network is designed to capture global–local mixed features. To align domain distributions and learn shared features, two corresponding disentanglers separate domain-invariant features and domain-specific features from mixed features. Furthermore, to allow domain-invariant features containing purer category discriminative information, class-invariant features are also segregated. In addition, an adversarial learning strategy between three features is utilized to simultaneously enhance the transferability and discriminability of domain-invariant features. The effectiveness of FDDAN is demonst...