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

被引:98
作者
Sonobe, Rei [1 ]
Tani, Hiroshi [2 ]
Wang, Xiufeng [2 ]
Kobayashi, Nobuyuki [3 ]
Shimamura, Hideki [4 ]
机构
[1] Hokkaido Univ, Grad Sch Agr, Sapporo, Hokkaido, Japan
[2] Hokkaido Univ, Res Fac Agr, Sapporo, Hokkaido, Japan
[3] Hokkaido Intellect Tank, Sapporo, Hokkaido, Japan
[4] PASCO Corp, Meguro Ku, Tokyo, Japan
关键词
ALOS PALSAR; SAR;
D O I
10.1080/2150704X.2014.889863
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
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 dual-polarimetric 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.
引用
收藏
页码:157 / 164
页数:8
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