Application of neural network swarm optimization for paddy-field classification from remote sensing data

Monitoring changes in paddy areas is important for economic and environmental research, since rice is a staple food in Asia and paddy agriculture is a major cropping system. Recently, remote sensing has been used to observe changes in the areas of paddy. However, monitoring paddy areas by remote sen...

Full description

Saved in:
Bibliographic Details
Published in:Artificial life and robotics 2012-02, Vol.16 (4), p.497-501
Main Authors: Mori, Kazuma, Yamaguchi, Takashi, Park, Jong Geol, Mackin, Kenneth J.
Format: Article
Language:English
Subjects:
Agriculture
Artificial Intelligence
Classification
Computation by Abstract Devices
Computer Science
Control
Mechatronics
Monitoring
Neural networks
Optimization
Original Article
Remote sensing
Robotics
Search methods
Staples
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Monitoring changes in paddy areas is important for economic and environmental research, since rice is a staple food in Asia and paddy agriculture is a major cropping system. Recently, remote sensing has been used to observe changes in the areas of paddy. However, monitoring paddy areas by remote sensing is difficult owing to the temporal changes in paddy, and the differences in the spatiotemporal characteristics of paddy agriculture between countries or regions. In our previous research using a multilayered perceptron and spatiotemporal satellite sensor data, the proposed classifier yielded a correct classification rate of 90.8%. In this article, we proposed a cooperative learning method using particle swarm optimization as the global search method and a multilayered perceptron as the local search method in order to improve the classification accuracy for practical use.
ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-011-0974-0