{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:05:15Z","timestamp":1787029515653,"version":"build-2736575974"},"reference-count":74,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,2,24]],"date-time":"2017-02-24T00:00:00Z","timestamp":1487894400000},"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":["61502195, 61472155"],"award-info":[{"award-number":["61502195, 61472155"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Macau Science and Technology Development Fund","award":["FDCT\/016\/2015\/A1"],"award-info":[{"award-number":["FDCT\/016\/2015\/A1"]}]},{"name":"Self-Determined Research Funds of CCNU From the Colleges\u2019 Basic Research and Operation of MOE","award":["CCNU14A05023, CCNU16A05022"],"award-info":[{"award-number":["CCNU14A05023, CCNU16A05022"]}]},{"name":"Research Committee at University of Macau","award":["MYRG2014-00003-FST, YRG2016-00123-FST"],"award-info":[{"award-number":["MYRG2014-00003-FST, YRG2016-00123-FST"]}]},{"name":"Postdoctoral Science Foundation of China","award":["2015M582223"],"award-info":[{"award-number":["2015M582223"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboringregions. SSRformsadeeparchitectureandisabletolearndiscriminativespectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single uni\ufb01ed framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classi\ufb01cation. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Re\ufb02ective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classi\ufb01cation.<\/jats:p>","DOI":"10.3390\/rs9030203","type":"journal-article","created":{"date-parts":[[2017,2,24]],"date-time":"2017-02-24T11:19:50Z","timestamp":1487935190000},"page":"203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Spectral-Spatial Response for Hyperspectral Image Classi\ufb01cation"],"prefix":"10.3390","volume":"9","author":[{"given":"Yantao","family":"Wei","sequence":"first","affiliation":[{"name":"School of Educational Information Technology, Central China Normal University, Wuhan 430079, China"},{"name":"Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4487-6384","authenticated-orcid":false,"given":"Yicong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7738","DOI":"10.1109\/TGRS.2014.2318058","article-title":"Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation","volume":"52","author":"Fang","year":"2014","journal-title":"IEEE Trans. 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