Heterogeneous Open-Set Cross-Domain Manifold Embedding Aligned for HSI-MSI Collaborative Classification

Hyperspectral images (HSI) have higher spectral resolution than multispectral images (MSI), but due to limitations of imaging equipment, their width is narrower than MSI. When using partially overlapping HSI-MSI to improve the classification capabilities of MSI, there may be unknown classes that do not exist in HSI-MSI overlapping regions. To solve this problem, this paper proposes a heterogeneous open-set cross-domain manifold embedding aligned method for HSI-MSI collaborative classification. The method designs manifold embedding to align HSI-MSI features to map into subspaces, and gradually selects target domain samples for pseudo-labeling through the designed strategy while rejecting unknown class samples. The feature alignment and pseudo-labeled sample selection are continuously iterated to promote each other, reducing the intra-class distance while pushing the rejected target data away from known classes. The experimental results verify the superiority of our method.