{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T05:53:50Z","timestamp":1769579630927,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T00:00:00Z","timestamp":1612310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.<\/jats:p>","DOI":"10.3390\/rs13040547","type":"journal-article","created":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T20:31:51Z","timestamp":1612384311000},"page":"547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5662-0394","authenticated-orcid":false,"given":"Wenning","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology of CAS, Xi\u2019an Institute of Optics and Precision Mechanics, CAS, Xi\u2019an 710119, China"},{"name":"Faculty of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Beijing 100049, China"},{"name":"School of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an, China"}]},{"given":"Xuebin","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology of CAS, Xi\u2019an Institute of Optics and Precision Mechanics, CAS, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, No.19(A) Yuquan Road, Beijing 100049, China"}]},{"given":"Xuanqin","family":"Mou","sequence":"additional","affiliation":[{"name":"Faculty of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/TGRS.2016.2616649","article-title":"Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection","volume":"55","author":"Zhang","year":"2016","journal-title":"IEEE Trans. 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