Extreme Learning Machine-based Crop Classification using ALOS/PALSAR Images
ISSN | 00213551 |
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NII recode ID (NCID) | 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.
Date of issued | |
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Creator | SONOBE Rei TANI Hiroshi WANG Xiufeng KOJIMA Yasuhito KOBAYASHI Nobuyuki |
Subject |
Hokkaido machine learning sigma naught |
Publisher | Japan International Research Center for Agricultural Sciences |
Available Online | |
NII resource type vocabulary | Journal Article |
Volume | 49 |
Issue | 4 |
spage | 377 |
epage | 381 |
DOI | 10.6090/jarq.49.377 |
Rights | Japan International Research Center for Agricultural Sciences |
Language | eng |