Open-Set Cross-Domain Hyperspectral Image Classification Based on Manifold Mapping Alignment

Cross-scene hyperspectral image (HSI) classification using transfer learning can effectively address the challenge of insufficient labeled samples in classification. The majority of existing transfer learning methods assume consistency in feature labels between the source and target HSI. In practical situations, the target-domain hyperspectral data are likely to contain some categories that do not exist in the source-domain hyperspectral data. In order to solve this problem, this article proposes an unsupervised open-set cross-domain manifold mapping aligned method for target-domain HSI classification. This method designs manifold embedding maps to align the spectral–spatial features of hyperspectral in different scenes. The spectral–spatial feature data are mapped to the subspace using both the source-domain and target-domain mapping matrices. Gradually, target-domain samples are chosen for pseudolabeling within the shared subspace, while samples from unknown classes are rejected. This method achieves subspace learning and pseudolabeled sample updating through iteration, reducing the intraclass distance of all the classes and pushing rejected target data away from known classes. Experiments on the three cross-domain hyperspectral data pairs demonstrate that the proposed method outperforms related state-of-the-art methods.