{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:46:45Z","timestamp":1778258805354,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T00:00:00Z","timestamp":1638835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB0505401"],"award-info":[{"award-number":["2018YFB0505401"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["(No. 41771452"],"award-info":[{"award-number":["(No. 41771452"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Director Fund of Institute of Remote Sensing and Digital Earth","award":["Y5SJ1500CX"],"award-info":[{"award-number":["Y5SJ1500CX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene complexity, as a remote sensing image always contains multiple categories of surface features. In this case, a single label cannot comprehensively describe the semantic content of a complex remote sensing image scene and therefore results in poor retrieval performance in practical applications. As a result, researchers have begun to pay attention to multi-label image retrieval. However, in the era of massive remote sensing data, how to increase retrieval efficiency and reduce feature storage while preserving semantic information remains unsolved. Considering the powerful capability of hashing learning in overcoming the curse of dimensionality caused by high-dimensional image representation in Approximate Nearest Neighbor (ANN) search problems, we propose a new semantic-preserving deep hashing model for multi-label remote sensing image retrieval. Our model consists of three main components: (1) a convolutional neural network to extract image features; (2) a hash layer to generate binary codes; (3) a new loss function to better maintain the multi-label semantic information of hash learning contained in context remote sensing image scene. As far as we know, this is the first attempt to apply deep hashing into the multi-label remote sensing image retrieval. Experimental results indicate the effectiveness and promising of the introduction of hashing methods in the multi-label remote sensing image retrieval.<\/jats:p>","DOI":"10.3390\/rs13244965","type":"journal-article","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T11:00:23Z","timestamp":1638874823000},"page":"4965","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Semantic-Preserving Deep Hashing Model for Multi-Label Remote Sensing Image Retrieval"],"prefix":"10.3390","volume":"13","author":[{"given":"Qimin","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9931-9884","authenticated-orcid":false,"given":"Haiyan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan","family":"Ye","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deqiao","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhuo","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"key":"ref_1","first-page":"1211","article-title":"Automatic analysis and mining of remote sensing big data","volume":"43","author":"Deren","year":"2014","journal-title":"Acta Geod. 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