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.

Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification / Qin, Y.; Bruzzone, L.; Li, B.. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 57:11(2019), pp. 9290-9307. [10.1109/TGRS.2019.2926069]

Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification

Qin Y.;Bruzzone L.;
2019-01-01

Abstract

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.
2019
11
Qin, Y.; Bruzzone, L.; Li, B.
Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification / Qin, Y.; Bruzzone, L.; Li, B.. - In: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING. - ISSN 0196-2892. - 57:11(2019), pp. 9290-9307. [10.1109/TGRS.2019.2926069]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/250897
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