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

JARQ : Japan Agricultural Research Quarterly
ISSN 00213551
書誌レコードID(総合目録DB) AA0068709X
本文フルテキスト

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
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
データ作成日
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
49
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