{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:26:08Z","timestamp":1773354368486,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101344"],"award-info":[{"award-number":["42101344"]}],"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":["62101179"],"award-info":[{"award-number":["62101179"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, state-of-the-art classification performance of natural images has been obtained by self-supervised learning (S2L) as it can generate latent features through learning between different views of the same images. However, the latent semantic information of similar images has hardly been exploited by these S2L-based methods. Consequently, to explore the potential of S2L between similar samples in hyperspectral image classification (HSIC), we propose the nearest neighboring self-supervised learning (N2SSL) method, by interacting between different augmentations of reliable nearest neighboring pairs (RN2Ps) of HSI samples in the framework of bootstrap your own latent (BYOL). Specifically, there are four main steps: pretraining of spectral spatial residual network (SSRN)-based BYOL, generation of nearest neighboring pairs (N2Ps), training of BYOL based on RN2P, final classification. Experimental results of three benchmark HSIs validated that S2L on similar samples can facilitate subsequent classification. Moreover, we found that BYOL trained on an un-related HSI can be fine-tuned for classification of other HSIs with less computational cost and higher accuracy than training from scratch. Beyond the methodology, we present a comprehensive review of HSI-related data augmentation (DA), which is meaningful to future research of S2L on HSIs.<\/jats:p>","DOI":"10.3390\/rs15061713","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T08:36:16Z","timestamp":1679474176000},"page":"1713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Nearest Neighboring Self-Supervised Learning for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Yao","family":"Qin","sequence":"first","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-6722","authenticated-orcid":false,"given":"Yuanxin","family":"Ye","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China"}]},{"given":"Junzheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}]},{"given":"Kenan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0012-7322","authenticated-orcid":false,"given":"Kun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Semi-supervised locality preserving dense graph neural network with ARMA filters and context-aware learning for hyperspectral image classification","volume":"60","author":"Ding","year":"2021","journal-title":"IEEE Trans. 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