{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T08:11:52Z","timestamp":1769501512550,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,29]],"date-time":"2019-06-29T00:00:00Z","timestamp":1561766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF-15-1-0521"],"award-info":[{"award-number":["W911NF-15-1-0521"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF-18-1-0457"],"award-info":[{"award-number":["W911NF-18-1-0457"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sparse representation classification (SRC) is being widely applied to target detection in hyperspectral images (HSI). However, due to the problem in HSI that high-dimensional data contain redundant information, SRC methods may fail to achieve high classification performance, even with a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is an important and challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature learning (DFL) method, which combines spectral and spatial information into a hypergraph Laplacian. First, a subset of discriminative features is selected, which preserve the spectral structure of data and the inter- and intra-class constraints on labeled training samples. A feature evaluator is obtained by semi-supervised learning with the hypergraph Laplacian. Secondly, the selected features are mapped into a further lower-dimensional eigenspace through a generalized eigendecomposition of the Laplacian matrix. The finally extracted discriminative features are used in a joint sparsity-model algorithm. Experiments conducted with benchmark data sets and different experimental settings show that our proposed method increases classification accuracy and outperforms the state-of-the-art HSI classification methods.<\/jats:p>","DOI":"10.3390\/rs11131552","type":"journal-article","created":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T03:23:59Z","timestamp":1561951439000},"page":"1552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Spectral\u2013Spatial Discriminant Feature Learning for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7026-6643","authenticated-orcid":false,"given":"Chunhua","family":"Dong","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA 31030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masoud","family":"Naghedolfeizi","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA 31030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawit","family":"Aberra","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA 31030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Fort Valley State University, Fort Valley, GA 31030, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.patcog.2016.10.019","article-title":"Hyperspectral image reconstruction by deep convolutional neural network for classification","volume":"63","author":"Li","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1109\/TGRS.2016.2642479","article-title":"Sparse Hilbert Schmidt Independence Criterion and Surrogate-Kernel-Based Feature Selection for Hyperspectral Image Classification","volume":"55","author":"Damodaran","year":"2017","journal-title":"IEEE Trans. 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