Extreme Learning Machine-based Crop Classification using ALOS/PALSAR Images
ISSN | 00213551 |
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書誌レコードID(総合目録DB) | AA0068709X |
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.
刊行年月日 | |
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作成者 | SONOBE Rei TANI Hiroshi WANG Xiufeng KOJIMA Yasuhito KOBAYASHI Nobuyuki |
著者キーワード |
Hokkaido machine learning sigma naught |
公開者 | Japan International Research Center for Agricultural Sciences |
オンライン掲載日 | |
国立情報学研究所メタデータ主題語彙集(資源タイプ) | Journal Article |
巻 | 49 |
号 | 4 |
開始ページ | 377 |
終了ページ | 381 |
DOI | 10.6090/jarq.49.377 |
権利 | Japan International Research Center for Agricultural Sciences |
言語 | eng |