{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T03:42:36Z","timestamp":1772077356517,"version":"3.50.1"},"reference-count":22,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","funder":[{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LY22F010010"],"award-info":[{"award-number":["LY22F010010"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701468"],"award-info":[{"award-number":["61701468"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071421"],"award-info":[{"award-number":["62071421"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0219691322500461","type":"journal-article","created":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T16:13:56Z","timestamp":1669133636000},"source":"Crossref","is-referenced-by-count":9,"title":["Cross-domain residual deep NMF for transfer learning between different hyperspectral image scenes"],"prefix":"10.1142","volume":"21","author":[{"given":"Ling","family":"Lei","sequence":"first","affiliation":[{"name":"Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, P. R. China"}]},{"given":"Binqian","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3608-7913","authenticated-orcid":false,"given":"Minchao","family":"Ye","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, P. R. China"}]},{"given":"Futian","family":"Yao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, P. R. China"}]},{"given":"Yuntao","family":"Qian","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University, Hangzhou 310027, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"S0219691322500461BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2964627"},{"key":"S0219691322500461BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3000677"},{"key":"S0219691322500461BIB003","first-page":"1096","volume-title":"Proc. IEEE Int. Geoscience and Remote Sensing Symp.","author":"Chen H.","year":"2019"},{"key":"S0219691322500461BIB004","doi-asserted-by":"publisher","DOI":"10.3390\/e20090714"},{"key":"S0219691322500461BIB005","first-page":"5542054","volume":"2022","author":"Guariglia E.","year":"2022","journal-title":"J. Funct. Spaces"},{"key":"S0219691322500461BIB006","first-page":"337","volume":"179","author":"Guariglia E.","year":"2016","journal-title":"Proc. Eng. Math. 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