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- W4309595679 abstract "Hyperspectral image (HSI) classification has long been a hot research topic. Most previous researches concentrate on the classification task of a single HSI scene, called single-scene classification. This research focuses on two closely related HSI scenes (called source and target scenes, respectively), and the problem is named cross-scene classification. This paper aims to explore the shared feature sub-space between two HSI scenes. A transfer learning algorithm called cross-domain residual deep nonnegative matrix factorization (CDRDNMF) is proposed. CDRDNMF is a multi-layer architecture consisting of dual-dictionary nonnegative matrix factorization (DDNMF) layers. In each layer, DDNMF is performed on source and target features for domain-invariant feature extraction. Then a data recovery process is completed, and the residual components from the recovery are passed to the next layer after activation. With such a multi-layer architecture, CDRDNMF delivers knowledge transfer and multi-scale feature extraction tasks. The experimental results prove the excellent performance of CDRDNMF on cross-scene classification." @default.
- W4309595679 created "2022-11-28" @default.
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- W4309595679 date "2022-11-21" @default.
- W4309595679 modified "2023-10-14" @default.
- W4309595679 title "Cross-domain residual deep NMF for transfer learning between different hyperspectral image scenes" @default.
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- W4309595679 doi "https://doi.org/10.1142/s0219691322500461" @default.
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