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An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/64502

Title: An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data
Authors: Sonobe, Rei Browse this author
Tani, Hiroshi Browse this author →KAKEN DB
Wang, Xiufeng Browse this author →KAKEN DB
Keywords: Classification and regression tree
kernel-based extreme learning machine
Landsat-8
operational land imager
Issue Date: 2016
Publisher: Taylor & Francis
Journal Title: Geocarto international
Volume: 32
Issue: 2
Start Page: 128
End Page: 138
Publisher DOI: 10.1080/10106049.2015.1130085
Abstract: The operational land imager (OLI) is the latest instrument in the Landsat series of satellite imagery, which officially began normal operations on 30 May 2013. The OLI includes two bands that are not on the thematic mapper series of sensors aboard Landsat-5 and 7; a cirrus band and a coastal/aerosol band. This paper compares the classification and regression tree and the kernel-based extreme learning machine (KELM) for mapping crops in Hokkaido, Japan, using OLI data, except the cirrus band and the pan band. The OLI data acquired on 8 July 2013 was used for crop classification of beans, beets, grassland, maize, potatoes and winter wheat. The KELM algorithm performed better in this study and achieved overall accuracies of 90.1%. According to the Jeffries-Matusita (J-M) distances, the short wavelength infrared band provides the greater contribution (the highest value was observed for band 6 in OLI data).
Rights: This is an Accepted Manuscript of an article published by Taylor & Francis in Geocarto international on 12 Jan 2016, available online: http://www.tandfonline.com/10.1080/10106049.2015.1130085
Type: article (author version)
URI: http://hdl.handle.net/2115/64502
Appears in Collections:農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 薗部 礼

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