{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T11:11:50Z","timestamp":1769857910816,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"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":["61972240"],"award-info":[{"award-number":["61972240"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20050501900"],"award-info":[{"award-number":["20050501900"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Program for the Capacity Development of Shanghai Local Colleges","award":["61972240"],"award-info":[{"award-number":["61972240"]}]},{"name":"Program for the Capacity Development of Shanghai Local Colleges","award":["20050501900"],"award-info":[{"award-number":["20050501900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development of remote sensing technology, content-based remote sensing image retrieval has become a research hotspot. Remote sensing image datasets not only contain rich location, semantic and scale information but also have large intra-class differences. Therefore, the key to improving the performance of remote sensing image retrieval is to make full use of the limited sample information to extract more comprehensive class features. In this paper, we propose a proxy-based deep metric learning method and an adaptive multi-proxy framework. First, we propose an intra-cluster sample synthesis strategy with a random factor, which uses the limited samples in batch to synthesize more samples to enhance the network\u2019s learning of unobvious features in the class. Second, we propose an adaptive proxy assignment method to assign multiple proxies according to the cluster of samples within a class, and to determine weights for each proxy according to the cluster scale to accurately and comprehensively measure the sample-class similarity. Finally, we incorporate a rigorous evaluation metric mAP@R and a variety of dataset partitioning methods, and conduct extensive experiments on commonly used remote sensing image datasets.<\/jats:p>","DOI":"10.3390\/rs14215615","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T08:17:12Z","timestamp":1667895432000},"page":"5615","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Adaptive Multi-Proxy for Remote Sensing Image Retrieval"],"prefix":"10.3390","volume":"14","author":[{"given":"Xinyue","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0604-5563","authenticated-orcid":false,"given":"Song","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanling","family":"Du","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengying","family":"Ge","sequence":"additional","affiliation":[{"name":"Engineering Training Centre, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chang, S., Wang, Z., Mao, D., Guan, K., and Chen, C. 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