国立研究開発法人 国際農林水産業研究センター | JIRCAS

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2015-10-01
作成者SONOBE ReiTANI HiroshiWANG XiufengKOJIMA YasuhitoKOBAYASHI 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2015-10-01
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
DOI10.6090/jarq.49.377
権利Japan International Research Center for Agricultural Sciences
言語eng