{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:27:16Z","timestamp":1783628836897,"version":"3.55.0"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T00:00:00Z","timestamp":1640649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Advance Research Project of Civil Space Technology","award":["D040402"],"award-info":[{"award-number":["D040402"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871226"],"award-info":[{"award-number":["41871226"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene classification. Though significant success has been achieved, these approaches are still subject to an excess of parameters and extremely dependent on a large quantity of labeled data. In this study, few-shot learning is used for remote sensing scene classification tasks. The goal of few-shot learning is to recognize unseen scene categories given extremely limited labeled samples. For this purpose, a novel task-adaptive embedding network is proposed to facilitate few-shot scene classification of remote sensing images, referred to as TAE-Net. A feature encoder is first trained on the base set to learn embedding features of input images in the pre-training phase. Then in the meta-training phase, a new task-adaptive attention module is designed to yield the task-specific attention, which can adaptively select informative embedding features among the whole task. In the end, in the meta-testing phase, the query image derived from the novel set is predicted by the meta-trained model with limited support images. Extensive experiments are carried out on three public remote sensing scene datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. The experimental results illustrate that our proposed TAE-Net achieves new state-of-the-art performance for few-shot remote sensing scene classification.<\/jats:p>","DOI":"10.3390\/rs14010111","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T06:55:03Z","timestamp":1640674503000},"page":"111","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0557-8912","authenticated-orcid":false,"given":"Wendong","family":"Huang","sequence":"first","affiliation":[{"name":"Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1668-6014","authenticated-orcid":false,"given":"Zhengwu","family":"Yuan","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6159-7395","authenticated-orcid":false,"given":"Aixia","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3839-9267","authenticated-orcid":false,"given":"Chan","family":"Tang","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5688-0324","authenticated-orcid":false,"given":"Xiaobo","family":"Luo","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.landurbplan.2010.12.009","article-title":"A case study on the relation between city planning and urban growth using remote sensing and spatial metrics","volume":"100","author":"Pham","year":"2011","journal-title":"Landsc. 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