{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T18:59:56Z","timestamp":1768071596680,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,5]],"date-time":"2019-03-05T00:00:00Z","timestamp":1551744000000},"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":["51704115"],"award-info":[{"award-number":["51704115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019081","name":"Science and Technology Program of Hunan Province","doi-asserted-by":"publisher","award":["2016TP1021"],"award-info":[{"award-number":["2016TP1021"]}],"id":[{"id":"10.13039\/501100019081","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan 235 Provincial Innovation Foundation For Postgraduate","award":["CX2018B771"],"award-info":[{"award-number":["CX2018B771"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spectral features cannot effectively reflect the differences among the ground objects and distinguish their boundaries in hyperspectral image (HSI) classification. Multi-scale feature extraction can solve this problem and improve the accuracy of HSI classification. The Gaussian pyramid can effectively decompose HSI into multi-scale structures, and efficiently extract features of different scales by stepwise filtering and downsampling. Therefore, this paper proposed a Gaussian pyramid based multi-scale feature extraction (MSFE) classification method for HSI. First, the HSI is decomposed into several Gaussian pyramids to extract multi-scale features. Second, we construct probability maps in each layer of the Gaussian pyramid and employ edge-preserving filtering (EPF) algorithms to further optimize the details. Finally, the final classification map is acquired by a majority voting method. Compared with other spectral-spatial classification methods, the proposed method can not only extract the characteristics of different scales, but also can better preserve detailed structures and the edge regions of the image. Experiments performed on three real hyperspectral datasets show that the proposed method can achieve competitive classification accuracy.<\/jats:p>","DOI":"10.3390\/rs11050534","type":"journal-article","created":{"date-parts":[[2019,3,5]],"date-time":"2019-03-05T11:19:50Z","timestamp":1551784790000},"page":"534","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Hyperspectral Image Classification with Multi-Scale Feature Extraction"],"prefix":"10.3390","volume":"11","author":[{"given":"Bing","family":"Tu","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Hunan Institute of Science and Technology, Yueyang 414006, China"},{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"}]},{"given":"Nanying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hunan Institute of Science and Technology, Yueyang 414006, China"}]},{"given":"Leyuan","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, Changsha 410082, China"}]},{"given":"Danbing","family":"He","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hunan Institute of Science and Technology, Yueyang 414006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1203-741X","authenticated-orcid":false,"given":"Pedram","family":"Ghamisi","sequence":"additional","affiliation":[{"name":"Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF), Exploration, 09599 Freiberg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2731","DOI":"10.1016\/j.patcog.2008.04.013","article-title":"Statistical pattern recognition in remote sensing","volume":"41","author":"Chen","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.patcog.2007.04.003","article-title":"Corrigendum to Real-time line detection through an improved hough transform voting scheme","volume":"41","author":"Fernandes","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/JSTARS.2015.2489207","article-title":"Quantitative detection of settled dust over green canopy using sparse unmixing of airborne hyperspectral data","volume":"9","author":"Brook","year":"2016","journal-title":"IEEE J. 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