Application of Artificial Neural Network for Paddy Field Classification using Spatiotemporal Information

被引：0
|
作者：
Yamaguchi, Takashi [1 ]
Kishida, Kazuya [1 ]
Nunohiro, Eiji [1 ]
Park, Jong Geol [2 ]
Mackin, Kenneth J. [1 ]
Hara, Keitaro [2 ]
Matsushita, Kotaro [2 ]
Harada, Ippei [2 ]
机构：
[1] Tokyo Univ Informat Sci, Dept Informat Syst, Wakaba Ku, Chiba 2658501, Japan
[2] Tokyo Univ Informat Sci, Dept Environm Informat, Wakaba Ku, Chiba 2658501, Japan
来源：
INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL | 2010年 / 13卷 / 3B期
关键词：
Artificial neural network; Classification; Remote sensing; MODIS;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Monitoring changes in paddy field area is important in Asia. For monitoring change in land surface, various applications using different satellites were researched in the field of remote sensing. However monitoring paddy field area with remote sensing is difficult due to the temporal change in land surface, and difference of spatiotemporal characteristics in countries and regions. In this paper, we applied artificial neural network to classify paddy field areas using moderate resolution sensor data that includes spatiotemporal information. Our aim is to automatically generate a paddy field classifier in order to create localized classifiers for each country and region.
引用
收藏
页码：967 / 974
页数：8
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