{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:25:21Z","timestamp":1774927521849,"version":"3.50.1"},"reference-count":18,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing is used in an increasingly wide range of applications. Models and methodologies based on artificial intelligence (AI) are commonly used to increase the performance of remote sensing technologies. Deep learning (DL) models are the most widely researched AI-based models because of their effectiveness and high performance. Therefore, we organized a Special Issue on remote sensing titled \u201cArtificial Intelligence and Machine Learning Applications in Remote Sensing.\u201d In this paper, we review nine articles included in this Special Issue, most of which report studies based on satellite data and DL, reflecting the most prevalent trends in remote sensing research, as well as how DL architecture and the functioning of DL models can be analyzed and explained is a hot topic in AI research. DL methods can outperform conventional machine learning methods in remote sensing; however, DL remains a black box and understanding the details of the mechanisms through which DL models make decisions is difficult. Therefore, researchers must continue to investigate how explainable DL methods for use in the field of remote sensing can be developed.<\/jats:p>","DOI":"10.3390\/rs15030569","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T03:04:44Z","timestamp":1674011084000},"page":"569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Special Issue Review: Artificial Intelligence and Machine Learning Applications in Remote Sensing"],"prefix":"10.3390","volume":"15","author":[{"given":"Ying-Nong","family":"Chen","sequence":"first","affiliation":[{"name":"Center for Space and Remote Sensing Research, National Central University, No. 300, Jhongda Rd., Jhongli Dist., Taoyuan City 32001, Taiwan"},{"name":"Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Rd., Jhongli Dist., Taoyuan City 32001, Taiwan"}]},{"given":"Kuo-Chin","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Rd., Jhongli Dist., Taoyuan City 32001, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5834-1057","authenticated-orcid":false,"given":"Yang-Lang","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}]},{"given":"Toshifumi","family":"Moriyama","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-Machi, Nagasaki 852-8521, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. 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