{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:33:33Z","timestamp":1776443613938,"version":"3.51.2"},"reference-count":52,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T00:00:00Z","timestamp":1609372800000},"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":["61871460"],"award-info":[{"award-number":["61871460"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Provincial Key Research and Development Program","award":["2020KW-003"],"award-info":[{"award-number":["2020KW-003"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3102019ghxm016"],"award-info":[{"award-number":["3102019ghxm016"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural network (CNN) based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method yields state-of-the-art performance. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale, the impact of different metrics and the number of support shots; the experiment results confirm that our model is specifically effective in few-shot settings.<\/jats:p>","DOI":"10.3390\/rs13010108","type":"journal-article","created":{"date-parts":[[2020,12,31]],"date-time":"2020-12-31T14:31:49Z","timestamp":1609425109000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Few-Shot Classification of Aerial Scene Images via Meta-Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6372-5653","authenticated-orcid":false,"given":"Pei","family":"Zhang","sequence":"first","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6923-672X","authenticated-orcid":false,"given":"Yunpeng","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, The University of Melbourne, Parkville, VIC 3010, Australia"}]},{"given":"Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Bendu","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Communication and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6026","DOI":"10.3390\/rs5116026","article-title":"Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use\/Cover Mapping","volume":"5","author":"Hu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","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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