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Recent applications pose an emerging need of analyzing the land cover types based on high resolution hyperspectral remote sensing data originating from remote sensory devices. Toward this goal, we propose a deep neural network (DNN) classifier in this paper. The DNN is constructed by combining a stacked autoencoder with desired numbers of autoencoders and a softmax classifier. Our experimental results based on the hyperspectral remote sensing data demonstrate that the presented DNN classifier can accurately distinguish different land covers including the mixed deciduous broadleaf natural forest and different land covers such as agriculture, roads, buildings, etc. We test the proposed method by using three different benchmark data sets. The proposed method showcases the huge potential of deep neural networks for hyperspectral data analysis.<\/jats:p>","DOI":"10.3233\/jifs-171307","type":"journal-article","created":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T11:04:04Z","timestamp":1524222244000},"page":"2273-2285","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":21,"title":["Classification of high resolution hyperspectral remote sensing data using\u00a0deep neural networks"],"prefix":"10.1177","volume":"34","author":[{"given":"Mehmet Emin","family":"Yuksel","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Engineering Faculty, Erciyes University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nurcan Sarikaya","family":"Basturk","sequence":"additional","affiliation":[{"name":"Department of Aircraft Electric and Electronics, Faculty of Aeronautics and Astronautics, Erciyes University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hasan","family":"Badem","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Engineering Faculty, Erciyes University, Turkey"},{"name":"Department of Computer Engineering, Engineering Faculty, Kahramanmaras Sutcu Imam University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Caliskan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Engineering Faculty, Erciyes University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alper","family":"Basturk","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Engineering Faculty, Erciyes University, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,4,19]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Advanced spaceborne thermal emission and reflection radiometer (2013) https:\/\/asterweb.jpl.nasa.gov\/"},{"key":"e_1_3_1_3_2","unstructured":"Hyperspectral remote sensing scenes repository. computational intelligence university of the basque country (2017)."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.05.061"},{"key":"e_1_3_1_5_2","article-title":"Artech House","author":"Benediktsson J.A.","year":"2015","unstructured":"BenediktssonJ.A. and GhamisiP., Artech House, Spectral-Spatial Classification of Hyperspectral Remote Sensing Images (2015).","journal-title":"Spectral-Spatial Classification of Hyperspectral Remote Sensing Images"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2004.842478"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"e_1_3_1_8_2","first-page":"3311","article-title":"Diagnosis of the parkinson disease by using deep neural network classifier","volume":"17","author":"Caliskan A.","year":"2017","unstructured":"CaliskanA., BademH., BasturkA. and YukselM.E., Diagnosis of the parkinson disease by using deep neural network classifier, Istanbul University - Journal of Electrical & Electronics Engineering 17 (2017), 3311\u20133318.","journal-title":"Istanbul University - Journal of Electrical & Electronics Engineering"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.5755\/j01.eie.23.2.18002"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2017.09.002"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2012.08.029"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/MGRS.2016.2616418"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11220-015-0126-z"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2011.629637"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1980-0572855-7"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_3_1_17_2","first-page":"566","article-title":"The spectrum kernel: A string kernel for svm protein classification","volume":"7","author":"Leslie C.S.","year":"2002","unstructured":"LeslieC.S., EskinE. and NobleW.S., The spectrum kernel: A string kernel for svm protein classification, Pacific symposium on biocomputing (2002), in Vol. 7 pp 566\u2013575.","journal-title":"Pacific symposium on biocomputing"},{"key":"e_1_3_1_18_2","unstructured":"LichmanM. 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