{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:08:54Z","timestamp":1776884934280,"version":"3.51.2"},"reference-count":53,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2013FM036"],"award-info":[{"award-number":["ZR2013FM036"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["ZR2015FM011"],"award-info":[{"award-number":["ZR2015FM011"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["51974170"],"award-info":[{"award-number":["51974170"]}]},{"name":"Natural Science Foundation of Shandong Province, China","award":["52104164"],"award-info":[{"award-number":["52104164"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2013FM036"],"award-info":[{"award-number":["ZR2013FM036"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2015FM011"],"award-info":[{"award-number":["ZR2015FM011"]}]},{"name":"National Natural Science Foundation of China","award":["51974170"],"award-info":[{"award-number":["51974170"]}]},{"name":"National Natural Science Foundation of China","award":["52104164"],"award-info":[{"award-number":["52104164"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, few-shot remote sensing scene classification has attracted significant attention, aiming to obtain excellent performance under the condition of insufficient sample numbers. A few-shot remote sensing scene classification framework contains two phases: (i) the pre-training phase seeks to adopt base data to train a feature extractor, and (ii) the meta-testing phase uses the pre-training feature extractor to extract novel data features and design classifiers to complete classification tasks. Because of the difference in the data category, the pre-training feature extractor cannot adapt to the novel data category, named negative transfer problem. We propose a novel method for few-shot remote sensing scene classification based on shared class Sparse Principal Component Analysis (SparsePCA) to solve this problem. First, we propose, using self-supervised learning, to assist-train a feature extractor. We construct a self-supervised assisted classification task to improve the robustness of the feature extractor in the case of fewer training samples and make it more suitable for the downstream classification task. Then, we propose a novel classifier for the few-shot remote sensing scene classification named Class-Shared SparsePCA classifier (CSSPCA). The CSSPCA projects novel data features into subspace to make reconstructed features more discriminative and complete the classification task. We have conducted many experiments on remote sensing datasets, and the results show that the proposed method dramatically improves classification accuracy.<\/jats:p>","DOI":"10.3390\/rs14102304","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T21:52:11Z","timestamp":1652219531000},"page":"2304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Class-Shared SparsePCA for Few-Shot Remote Sensing Scene Classification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0255-2848","authenticated-orcid":false,"given":"Jiayan","family":"Wang","sequence":"first","affiliation":[{"name":"Qingdao Software Institute, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Network Security and Information Office, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2432-4726","authenticated-orcid":false,"given":"Xueqin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1498-2186","authenticated-orcid":false,"given":"Lei","family":"Xing","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1408-5514","authenticated-orcid":false,"given":"Bao-Di","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4785-791X","authenticated-orcid":false,"given":"Zongmin","family":"Li","sequence":"additional","affiliation":[{"name":"Qingdao Software Institute, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,10]]},"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":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"13436","DOI":"10.3390\/rs71013436","article-title":"Scale Issues Related to the Accuracy Assessment of Land Use\/Land Cover Maps Produced Using Multi-Resolution Data: Comments on \u201cThe Improvement of Land Cover Classification by Thermal Remote Sensing\u201d. 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