{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:44:10Z","timestamp":1760150650067,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T00:00:00Z","timestamp":1703462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"],"award-info":[{"award-number":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"]}]},{"name":"Guangxi Science and Technology Programme","award":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"],"award-info":[{"award-number":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"]}]},{"name":"Hubei Key R&amp;D Programme","award":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"],"award-info":[{"award-number":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"]}]},{"name":"Sichuan Key R&amp;D Programme","award":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"],"award-info":[{"award-number":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"]}]},{"name":"Shanxi Provincial Science and Technology Major Special Project","award":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"],"award-info":[{"award-number":["42090012","42271352","Gui Ke 2021AB30019","2022BAA048","2022YFN0031","2023YFN0022","2023YFS0381","202201150401020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The effectiveness of hashing methods in big data retrieval has been proved due to their merit in computational and storage efficiency. Recently, encouraged by the strong discriminant capability of deep learning in image representation, various deep hashing methodologies have emerged to enhance retrieval performance. However, maintaining the semantic richness inherent in remote sensing images (RSIs), characterized by their scene intricacy and category diversity, remains a significant challenge. In response to this challenge, we propose a novel two-stage deep metric and category-level semantic hashing network termed DMCH. First, it introduces a novel triple-selection strategy during the semantic metric learning process to optimize the utilization of triple-label information. Moreover, it inserts a hidden layer to enhance the latent correlation between similar hash codes via a designed category-level classification loss. In addition, it employs additional constraints to keep bit-uncorrelation and bit-balance of generated hash codes. Furthermore, a progressive coarse-to-fine hash code sorting scheme is used for superior fine-grained retrieval and more effective hash function learning. Experiment results on three datasets illustrate the effectiveness and superiority of the proposed method.<\/jats:p>","DOI":"10.3390\/rs16010090","type":"journal-article","created":{"date-parts":[[2023,12,25]],"date-time":"2023-12-25T23:00:12Z","timestamp":1703545212000},"page":"90","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DMCH: A Deep Metric and Category-Level Semantic Hashing Network for Retrieval in Remote Sensing"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9931-9884","authenticated-orcid":false,"given":"Haiyan","family":"Huang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Qimin","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Zhenfeng","family":"Shao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-382X","authenticated-orcid":false,"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, Emory University, Atlanta, GA 30322, USA"}]},{"given":"Liyuan","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/JPROC.2016.2598228","article-title":"Big data for remote sensing: Challenges and opportunities","volume":"104","author":"Chi","year":"2016","journal-title":"Proc. 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