Cross-Domain Hyperspectral Image Classification via Mamba–CNN and Knowledge Distillation

Domain adaptation (DA)-based cross-domain hyperspectral image (HSI) classification methods have garnered significant attention. The majority of DA techniques utilize models based on convolutional neural networks (CNNs) and Transformers for feature extraction. However, Transformers may struggle to capture local details in HSIs, while CNNs often underperform in handling long-range dependencies. Furthermore, many methods focus only on aligning marginal distributions while ignoring the consistency of interclass features, which may lead to feature confusion and degraded classification accuracy. To overcome the challenges mentioned, we propose a Mamba–CNN and knowledge distillation network (MKDnet). First, the network employs a feature extractor that integrates Mamba and CNN frameworks for cross-domain HSI classification, enabling the capture of both global and local features while effectively capturing long-range dependencies. Second, domain alignment is achieved through distribution alignment and graph alignment. In the distribution alignment phase, we design a knowledge distillation architecture that utilizes soft labels to enhance the understanding of relationships between classes, thereby improving the consistency of interclass features. In the graph alignment phase, we use graph convolution to capture connections between nodes and edges and transfer class-level topological relationships across domains. Finally, the classifier is used to obtain classification results, with consistency constraints applied to balance features between classes more effectively. Extensive experiments have demonstrated that MKDnet outperforms other state-of-the-art methods on three public cross-domain HSI datasets.