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

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Title: Random Forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data
Authors: Sonobe, Rei Browse this author
Tani, Hiroshi Browse this author →KAKEN DB
Wang, Xiufeng Browse this author →KAKEN DB
Kobayashi, Nobuyuki Browse this author
Shimamura, Hideki Browse this author
Keywords: crop
gamma nought
multi-temporal classification approach
Issue Date: Feb-2014
Publisher: Taylor&Francis
Journal Title: Remote Sensing Letters
Volume: 5
Issue: 2
Start Page: 157
End Page: 164
Publisher DOI: 10.1080/2150704X.2014.889863
Abstract: 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.
Type: article (author version)
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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