Feature disentanglement based domain adaptation network for cross-scene coastal wetland hyperspectral image classification
Ziqi Xin, Zhongwei Li, Mingming Xu, Leiquan Wang, Guangbo Ren, Jianbu Wang, Yabin Hu
Abstract
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 demonstrated by the experimental results obtained from three cross-scene unmanned aerial vehicle datasets collected in coastal wetlands and a publicly available dataset. We will make our datasets and source code publicly available upon acceptance of the paper.