{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:38:52Z","timestamp":1760402332574,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T00:00:00Z","timestamp":1579564800000},"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":["61841602"],"award-info":[{"award-number":["61841602"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Provincial Science and Technology Innovation Special Fund Project of Jilin Province","award":["20190302026GX"],"award-info":[{"award-number":["20190302026GX"]}]},{"name":"Jilin Province Development and Reform Commission Industrial Technology Research and Development Project","award":["2019C054-4"],"award-info":[{"award-number":["2019C054-4"]}]},{"name":"Higher Education Research Project of Jilin Association for Higher Education","award":["JGJX2018D10"],"award-info":[{"award-number":["JGJX2018D10"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JLU"],"award-info":[{"award-number":["JLU"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for content-based remote sensing image retrieval. We have improved the current metric learning methods from the following aspects\u2014sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the fine-tuning network. The experiment results show that the method used in this paper achieves the state-of-the-art performance.<\/jats:p>","DOI":"10.3390\/ijgi9020061","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T11:25:59Z","timestamp":1579605959000},"page":"61","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval"],"prefix":"10.3390","volume":"9","author":[{"given":"Hongwei","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9081-4950","authenticated-orcid":false,"given":"Lin","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Editorial Department of Journal (Engineering and Technology Edition), Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.patcog.2006.04.045","article-title":"A survey of content-based image retrieval with high-level semantics","volume":"40","author":"Liu","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dharani, T., and Aroquiaraj, I.L. 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