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 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.

Authors and Affiliations

1. Department of Information Systems, Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-ku, Chiba, 265-8501, Japan

   Kazuma Mori, Takashi Yamaguchi & Kenneth J. Mackin

2. Department of Environmental Information, Tokyo University of Information Science, Chiba, Japan

   Jong Geol Park

Corresponding author

Correspondence to Kenneth J. Mackin.