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Extreme Learning Machine-based Crop Classification using ALOS/PALSAR Images
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)
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Submitter: 薗部 礼
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