{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:48:46Z","timestamp":1780318126926,"version":"3.54.1"},"reference-count":60,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Major Project for High Resolution Earth Observation System","award":["80-Y50G19-9001-22\/23"],"award-info":[{"award-number":["80-Y50G19-9001-22\/23"]}]},{"name":"National Science and Technology Major Project for High Resolution Earth Observation System","award":["241111210300"],"award-info":[{"award-number":["241111210300"]}]},{"name":"Henan Province Key Research and Development Special Project","award":["80-Y50G19-9001-22\/23"],"award-info":[{"award-number":["80-Y50G19-9001-22\/23"]}]},{"name":"Henan Province Key Research and Development Special Project","award":["241111210300"],"award-info":[{"award-number":["241111210300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Few-shot hyperspectral image classification aims to develop the ability of classifying image pixels by using relatively few labeled pixels per class. However, due to the inaccuracy of the localization system and the bias of the ground survey, the potential noisy labels in the training data pose a very significant challenge to few-shot hyperspectral image classification. To solve this problem, this paper proposes a weighted contrastive prototype network (WCPN) for few-shot hyperspectral image classification with noisy labels. WCPN first utilizes a similarity metric to generate the weights of the samples from the same classes, and applies them to calibrate the class prototypes of support and query sets. Then the weighted prototype network will minimize the distance between features and prototypes to train the network. WCPN also incorporates a weighted contrastive regularization function that uses the sample weights as gates to filter the fake positive samples whose labels are incorrect to further improve the discriminative power of the prototypes. We conduct experiments on multiple hyperspectral image datasets with artificially generated noisy labels, and the results show that the WCPN has excellent performance that can sufficiently mitigate the impact of noisy labels.<\/jats:p>","DOI":"10.3390\/rs16183527","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T03:49:46Z","timestamp":1727149786000},"page":"3527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Weighted Contrastive Prototype Network for Few-Shot Hyperspectral Image Classification with Noisy Labels"],"prefix":"10.3390","volume":"16","author":[{"given":"Dan","family":"Zhang","sequence":"first","affiliation":[{"name":"The College of Surveying and Mapping Engineering, Yellow River Conservancy Technical Institute, Kaifeng 475004, China"},{"name":"Henan Province Surveying and Mapping Real Scene 3D Technology Engineering Research Center, Yellow River Conservancy Technical Institute, Kaifeng 475004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9361-2991","authenticated-orcid":false,"given":"Yiyuan","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9993-3382","authenticated-orcid":false,"given":"Zhigang","family":"Han","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Henan University, Zhengzhou 450046, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1300\/J064v22n03_03","article-title":"Relationship between hyperspectral reflectance, soil nitrate-nitrogen, cotton leaf chlorophyll, and cotton yield: A step toward precision agriculture","volume":"22","author":"Boggs","year":"2003","journal-title":"J. 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