乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T08:03:47Z","timestamp":1742803427605,"version":"3.37.3"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61520106001"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61501180"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771192","61471167"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fund of Hunan Province for Science and Technology Plan Project","award":["2017RS3024"]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006667","name":"Ministerio de Educaci\u00f3n","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006667","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100014181","name":"Junta de Extremadura","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100014181","id-type":"DOI","asserted-by":"crossref"}]},{"name":"MINECO","award":["TIN2015-63646-C5-5-R"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Geosci. Remote Sensing"],"published-print":{"date-parts":[[2019,2]]},"DOI":"10.1109\/tgrs.2018.2860464","type":"journal-article","created":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T18:34:19Z","timestamp":1534530859000},"page":"755-769","source":"Crossref","is-referenced-by-count":217,"title":["Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification"],"prefix":"10.1109","volume":"57","author":[{"given":"Nanjun","family":"He","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1030-3729","authenticated-orcid":false,"given":"Mercedes E.","family":"Paoletti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6701-961X","authenticated-orcid":false,"given":"Juan Mario","family":"Haut","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2351-4461","authenticated-orcid":false,"given":"Leyuan","family":"Fang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0585-9848","authenticated-orcid":false,"given":"Shutao","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-1659","authenticated-orcid":false,"given":"Antonio","family":"Plaza","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2384-9141","authenticated-orcid":false,"given":"Javier","family":"Plaza","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7730321"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2014.7026039"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2014.2329330"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2843525"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2017.2762307"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1080\/2150704X.2015.1047045"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2517204"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2636241"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2675902"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2017.2755639"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2737037"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2016.2542193"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2584107"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2616355"},{"key":"ref20","first-page":"1063","article-title":"Cloud implementation of logistic regression for hyperspectral image classification","author":"haut","year":"2017","journal-title":"Proc 17th Int Conf Comput Math Methods Sci Eng"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.831865"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-018-0793-9"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2611503"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.08.019"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2768479"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2561842"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2801387"},{"key":"ref51","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"glorot","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/258619"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.11.021"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1117\/12.943611","article-title":"ROSIS (Reflective Optics System Imaging Spectrometer)—A candidate instrument for polar platform missions","volume":"886","author":"kunkel","year":"1988","journal-title":"Proc SPIE"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/S0034-4257(98)00064-9"},{"key":"ref52","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2838665"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2017.8127331"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2836307"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2798162"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.842478"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2048116"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2013.2264508"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2845668"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-016-1896-3"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2060550"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2799324"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2809606"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2830403"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2725580"},{"key":"ref8","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/36.3001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2014.2318058"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2015.2478379"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1080\/2150704X.2017.1280200"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2705073"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2017.2748160"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.3390\/rs8020099"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2016.7730324"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"shao","year":"2016","journal-title":"IEEE Trans Geosci Remote Sens"}],"container-title":["IEEE Transactions on Geoscience and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/36\/8620597\/08439081.pdf?arnumber=8439081","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T20:41:23Z","timestamp":1657744883000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8439081\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2]]},"references-count":56,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tgrs.2018.2860464","relation":{},"ISSN":["0196-2892","1558-0644"],"issn-type":[{"type":"print","value":"0196-2892"},{"type":"electronic","value":"1558-0644"}],"subject":[],"published":{"date-parts":[[2019,2]]}}}