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Experimental results using Hyperion, AVIRIS, and ROSIS hyperspectral data demonstrated that the SDAE pretraining in conjunction with the LR fine-tuning and classification (SDAE_LR) can achieve higher accuracies than the popular support vector machine (SVM) classifier.<\/jats:p>","DOI":"10.1155\/2016\/3632943","type":"journal-article","created":{"date-parts":[[2015,11,30]],"date-time":"2015-11-30T16:39:31Z","timestamp":1448901571000},"page":"1-10","source":"Crossref","is-referenced-by-count":147,"title":["Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images"],"prefix":"10.1155","volume":"2016","author":[{"given":"Chen","family":"Xing","sequence":"first","affiliation":[{"name":"Faculty of Mechanical and Electronic Information, China University of Geosciences, Wuhan, Hubei 430074, China"}]},{"given":"Li","family":"Ma","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical and Electronic Information, China University of Geosciences, Wuhan, Hubei 430074, China"}]},{"given":"Xiaoquan","family":"Yang","sequence":"additional","affiliation":[{"name":"Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2010.2055876"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2012.2202912"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2014.2365676"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2014.2388434"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.09.005"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.12.016"},{"key":"8","first-page":"153","volume-title":"Greedy layer-wise training of deep networks","volume":"19","year":"2007"},{"key":"11","first-page":"1097","volume-title":"Imagenet classification with deep convolutional neural networks","volume":"25","year":"2012"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/jstars.2014.2329330"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2014.2357078"},{"key":"16","first-page":"3371","volume":"11","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"18","first-page":"9","volume-title":"Online algorithms and stochastic approximations","year":"1998"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2011.2162339"}],"container-title":["Journal of Sensors"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2016\/3632943.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2016\/3632943.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2016\/3632943.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2016,7,26]],"date-time":"2016-07-26T09:25:04Z","timestamp":1469525104000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.hindawi.com\/journals\/js\/2016\/3632943\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":14,"alternative-id":["3632943","3632943"],"URL":"https:\/\/doi.org\/10.1155\/2016\/3632943","relation":{},"ISSN":["1687-725X","1687-7268"],"issn-type":[{"value":"1687-725X","type":"print"},{"value":"1687-7268","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]}}}