Random Forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data

Sonobe, Rei; Tani, Hiroshi; Wang, Xiufeng; Kobayashi, Nobuyuki; Shimamura, Hideki

Random Forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data

Sonobe, Rei; Tani, Hiroshi; Wang, Xiufeng et al.

Remote Sensing Letters, 2014, 5(2), 157-164

Permalink : https://hdl.handle.net/2115/57984

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論文情報

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アクセス権	open access
URI	https://hdl.handle.net/2115/57984
タイトル	Random Forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data
著者	Sonobe, Rei ; Tani, Hiroshi ; Wang, Xiufeng ; Kobayashi, Nobuyuki ; Shimamura, Hideki
著者名 Sonobe, Rei
e-Rad_Researcher 80142701 著者名 Tani, Hiroshi
e-Rad_Researcher 30301873 著者名 Wang, Xiufeng
著者名 Kobayashi, Nobuyuki
著者名 Shimamura, Hideki
言語	英語
キーワード	crop
	gamma nought
	multi-temporal classification approach
	TerraSAR-X
発行日	2014-02
出版者	Taylor&Francis
収録物名	Remote Sensing Letters
巻(号)	5(2)
ページ	157-164
抄録	The classification maps are required for the management and the estimation of agricultural disaster compensation; however, those techniques have yet to be established. Some supervised learning models may allow accurate classification. In this study, the Random Forest (RF) classifier and the classification and regression tree (CART) were applied to evaluate the potential of multi-temporal TerraSAR-X dualpolarimetric data, on the StripMap mode, for the classification of crop type. Furthermore, comparisons of the two algorithms and polarizations were carried out. In the study area, beans, beet, grasslands, maize, potato and winter wheat were cultivated, and these crop types were classified using the data set acquired in 2009. The classification results of RF were superior to those of CART, and the overall accuracies were 0.91–0.93.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
PISSN	2150-704X
EISSN	2150-7058
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.1080/2150704X.2014.889863