Cross-scene hyperspectral image classification based on cross-domain feature extraction and category decision collaborative optimization

This is the authors' abstract. We don't add key points for this paper.

• This paper proposes a cross-scene hyperspectral image classification method based on cross-domain feature extraction and category decision collaborative optimization. • An adaptive feature discovery mechanism based on dynamic masking is designed to obtain feature space with multiple viewpoints. • A dual-stream convolutional cross-domain feature extraction method based on Mamba stream and ViT stream is constructed to learn key cross-domain features. • A category decision strategy based on dual-stream classifier co-optimization is implemented to complete the classification of the target domain without pseudo labels. • Experimental results show the effectiveness of the proposed algorithm. Cross-scene hyperspectral image classification aims to enable the model to complete the classification of unlabeled target domain data by learning from labeled source domain data. Aiming at the problem that most current cross-scene hyperspectral image classification algorithms do not fully consider the cross-domain feature representation and category decision boundary optimization, a cross-domain Feature Extraction and Category Decision collaborative optimization (FECD) network is proposed. First, an adaptive feature discovery based on dynamic masks is designed. In this mechanism, the dynamically scaled masks are applied to the 3D representation of source and target domain data to generate an informative feature space and enhance the cross-scene discrimination potential of the model. Second, a dual-stream convolutional cross-domain feature extraction based on Mamba stream and ViT stream is constructed. Long sequence modeling and convolutional attention mechanisms are used to capture cross-domain spectral features between pixel, and self-attention mechanisms and multi-scale convolution are used to excavate cross-domain space patterns of pixel. Finally, a category decision based on the co-optimization of dual-stream classifiers is implemented. The spectral and spatial boundaries learned by the dual streams are fused to optimize the category decision. Therefore, the risk of false labeling is avoided while obtaining more accurate category boundaries. Compared with seven state-of-the-art algorithms on three widely used datasets, FECD obtains better categorization results on three categorization metrics: OA, AA, and Kappa.