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Extreme Learning Machine-based Crop Classification using ALOS/PALSAR Images

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Title: Extreme Learning Machine-based Crop Classification using ALOS/PALSAR Images
Authors: Sonobe, Rei Browse this author →KAKEN DB
Tani, Hiroshi Browse this author
Wang, Xiufeng Browse this author
Kojima, Yasuhito Browse this author
Kobayashi, Nobuyuki Browse this author
Issue Date: 1-Oct-2015
Publisher: Tropical Agriculture Research Center, Ministry of Agriculture and Forestry
Journal Title: Japan agricultural research quarterly : JARQ
Volume: 49
Issue: 4
Start Page: 377
End Page: 381
Publisher DOI: 10.6090/jarq.49.377
Abstract: Classification maps are required for agricultural management and the estimation of agricultural disaster compensation. The extreme learning machine (ELM), a newly developed single hidden layer neural network is used as a supervised classifier for remote sensing classifications. In this study, the ELM was evaluated to examine its potential for multi-temporal ALOS/PALSAR images for the classification of crop type. In addition, the k-nearest neighbor algorithm (k-NN), one of the traditional classification methods, was also applied for comparison with the ELM. In the study area, beans, beets, grasses, maize, potato, and winter wheat were cultivated; and these crop types in each field were identified using a data set acquired in 2010. The result of ELM classification was superior to that of k-NN; and overall accuracy was 79.3%. This study highlights the advantages of ALOS/PALSAR images for agricultural field monitoring and indicates the usefulness of regular monitoring using the ALOS-2/PALSAR-2 system.
Type: article
URI: http://hdl.handle.net/2115/64519
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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