{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:48:52Z","timestamp":1774968532761,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High-end Foreign Experts Introduction Program","award":["G2022012010L"],"award-info":[{"award-number":["G2022012010L"]}]},{"name":"High-end Foreign Experts Introduction Program","award":["LH2023F034"],"award-info":[{"award-number":["LH2023F034"]}]},{"name":"High-end Foreign Experts Introduction Program","award":["2021"],"award-info":[{"award-number":["2021"]}]},{"name":"Heilongjiang Natural Science Foundation Project","award":["G2022012010L"],"award-info":[{"award-number":["G2022012010L"]}]},{"name":"Heilongjiang Natural Science Foundation Project","award":["LH2023F034"],"award-info":[{"award-number":["LH2023F034"]}]},{"name":"Heilongjiang Natural Science Foundation Project","award":["2021"],"award-info":[{"award-number":["2021"]}]},{"name":"Reserved Leaders of Heilongjiang Provincial Leading Talent Echelon","award":["G2022012010L"],"award-info":[{"award-number":["G2022012010L"]}]},{"name":"Reserved Leaders of Heilongjiang Provincial Leading Talent Echelon","award":["LH2023F034"],"award-info":[{"award-number":["LH2023F034"]}]},{"name":"Reserved Leaders of Heilongjiang Provincial Leading Talent Echelon","award":["2021"],"award-info":[{"award-number":["2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The deep network model relies on sufficient training samples to achieve superior processing performance, which limits its application in hyperspectral image (HSI) classification. In order to perform HSI classification with noisy labels, a robust weakly supervised feature learning (WSFL) architecture combined with multi-model attention is proposed. Specifically, the input noisy labeled data are first subjected to multiple groups of residual spectral attention models and multi-granularity residual spatial attention models, enabling WSFL to refine and optimize the extracted spectral and spatial features, with a focus on extracting clean samples information and reducing the model\u2019s dependence on labels. Finally, the fused and optimized spectral-spatial features are mapped to the multilayer perceptron (MLP) classifier to increase the constraint of the model on the noisy samples. The experimental results on public datasets, including Pavia Center, WHU-Hi LongKou, and HangZhou, show that WSFL is better at classifying noise labels than excellent models such as spectral-spatial residual network (SSRN) and dual channel residual network (DCRN). On Hangzhou dataset, the classification accuracy of WSFL is superior to DCRN by 6.02% and SSRN by 7.85%, respectively.<\/jats:p>","DOI":"10.3390\/rs15204994","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T08:10:19Z","timestamp":1697530219000},"page":"4994","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hyperspectral Images Weakly Supervised Classification with Noisy Labels"],"prefix":"10.3390","volume":"15","author":[{"given":"Chengyang","family":"Liu","sequence":"first","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2453-3691","authenticated-orcid":false,"given":"Haibin","family":"Wu","sequence":"additional","affiliation":[{"name":"Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5511118","DOI":"10.1109\/TGRS.2023.3272639","article-title":"AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification","volume":"61","author":"Paoletti","year":"2023","journal-title":"IEEE Trans. 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