期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 57, 期 11, 页码 9290-9307出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2926069
关键词
Hyperspectral imaging; Manifolds; Image reconstruction; Matrix decomposition; Task analysis; Domain adaptation (DA); hyperspectral image (HSI) classification; superpixel segmentation; tensor alignment (TA)
类别
资金
- Hyperspectral Image Analysis Group
- NSF
This paper presents a tensor alignment (TA) based domain adaptation (DA) method for hyperspectral image (HSI) classification. To be specific, HSIs in both domains are first segmented into superpixels, and tensors of both domains are constructed to include neighboring samples from a single superpixel. Then the subspace alignment (SA) between the two domains is achieved through alignment matrices, and the original tensors are projected as core tensors with lower dimensions into the invariant tensor subspace by applying projection matrices. To preserve the geometric information of original tensors, we employ a manifold regularization term for core tensors into the optimization process. The alignment matrices, projection matrices, and core tensors are solved in the framework of Tucker decomposition with an alternating optimization strategy. In addition, a postprocessing strategy is defined via pure samples extraction for each superpixel to further improve classification performance. Experimental results on four real HSIs demonstrate that the proposed method can achieve better performance compared with the state-of-the-art subspace learning methods when a limited amount of source labeled samples are available.
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