Artificial neural networks paddy-field classifier using spatiotemporal remote sensing data

• Published in: Artificial life and robotics Vol. 15; no. 2; pp. 221 - 224
• Main Authors: Yamaguchi, Takashi, Kishida, Kazuya, Nunohiro, Eiji, Park, Jong Geol, Mackin, Kenneth J., Hara, Keitaro, Matsushita, Kotaro, Harada, Ippei
• Format: Journal Article
• Language: English
• Published: Japan Springer Japan 01-09-2010
• Subjects: Artificial Intelligence; Artificial neural networks; Classification; Classifiers; Computation by Abstract Devices; Computer Science; Control; Foods; Land; Mechatronics; Monitoring; Original Article; Remote sensing; Robotics; Staples; Artificial neural network; Remote sensing; MODIS; Classification
• ISSN: 1433-5298; 1614-7456
• DOI: 10.1007/s10015-010-0797-4

Monitoring changes in a paddy-field area is important since rice is a staple food and paddy agriculture is a major cropping system in Asia. For monitoring changes in land surface, various applications using different satellites have been researched in the field of remote sensing. However, monitoring a paddy-field area with remote sensing is difficult owing to the temporal changes in the land surface, and the differences in the spatiotemporal characteristics in countries and regions. In this article, we used an artificial neural network to classify paddy-field areas using moderate resolution sensor data that includes spatiotemporal information. Our aim is to automatically generate a paddy-field classifier in order to create localized classifiers for each country and region.