Contrastive Constrained Cross-Scene Model- Informed Interpretable Classification Strategy for Hyperspectral and LiDAR Data

Domain adaptation (DA) aims to transfer knowledge from a labeled source domain (SD) to an unlabeled target domain (TD), and its effectiveness has been demonstrated in unsupervised multisource remote sensing image classification. Existing DA frameworks simultaneously learn the mapping between data in SD and category labels, as well as minimize the distribution discrepancy between different domains. However, significant computational resources are needed to optimize the dual objectives in a data-driven DA network. In addition, the lack of interpretability of deep learning (DL)-based methods results in unpredictable feature distributions, thereby impeding the smooth update of the network in the desired direction. To address these issues, we propose a contrastive constrained cross-scene model-informed interpretable classification strategy (C3MI-C) for hyperspectral image (HSI) and light detection and ranging (LiDAR), which achieves a model-interpretable decoupling of domain adaptive task and classification task. The proposed C3MI-C optimizes the classification network interpretably in the same subspace and further aligns deep-adapted features extracted from two domains to accomplish high-precision unsupervised cross-scene classification. Comparative experiment results and ablation studies show that C3MI-C performs better than other advanced methods.