{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T03:51:31Z","timestamp":1769917891788,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T00:00:00Z","timestamp":1613347200000},"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":["62076204"],"award-info":[{"award-number":["62076204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M613204 and 2017M623246"],"award-info":[{"award-number":["2017M613204 and 2017M623246"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the superior spatial\u2013spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs). However, most CNN-based methods are supervised while the class labels of HSIs are limited and difficult to obtain. While a few unsupervised CNN-based methods have been proposed recently, they always focus on data reconstruction and are lacking in the exploration of discriminability which is usually the primary goal of DR. To address these issues, we propose a deep fully convolutional embedding network (DFCEN), which not only considers data reconstruction but also introduces the specific learning task of enhancing feature discriminability. DFCEN has an end-to-end symmetric network structure that is the key for unsupervised learning. Moreover, a novel objective function containing two terms\u2014the reconstruction term and the embedding term of a specific task\u2014is established to supervise the learning of DFCEN towards improving the completeness and discriminability of low-dimensional data. In particular, the specific task is designed to explore and preserve relationships among samples in HSIs. Besides, due to the limited training samples, inherent complexity and the presence of noise in HSIs, a preprocessing where a few noise spectral bands are removed is adopted to improve the effectiveness of unsupervised DFCEN. Experimental results on three well-known hyperspectral datasets and two classifiers illustrate that the low dimensional features of DFCEN are highly separable and DFCEN has promising classification performance compared with other DR methods.<\/jats:p>","DOI":"10.3390\/rs13040706","type":"journal-article","created":{"date-parts":[[2021,2,15]],"date-time":"2021-02-15T03:22:26Z","timestamp":1613359346000},"page":"706","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Fully Convolutional Embedding Networks for Hyperspectral Images Dimensionality Reduction"],"prefix":"10.3390","volume":"13","author":[{"given":"Na","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi\u2019an 710072, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi\u2019an 710072, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1124-6738","authenticated-orcid":false,"given":"Jiao","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi\u2019an 710072, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6925-109X","authenticated-orcid":false,"given":"Tao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, 127 West Youyi Road, Xi\u2019an 710072, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0415-8556","authenticated-orcid":false,"given":"Maoguo","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, Shaanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1109\/TGRS.2011.2178419","article-title":"Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors","volume":"50","author":"Murphy","year":"2012","journal-title":"IEEE Trans. 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