open access

https://hdl.handle.net/2115/64502

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

英語

operational land imager

2016

Taylor & Francis

Geocarto international

32(2)

128-138

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).

学術雑誌論文

Accepted Manuscript

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

1010-6049