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

  • Authors:

  • Takashi Yamaguchi;Kazuya Kishida;Eiji Nunohiro;Jong Geol Park;Kenneth J. Mackin;Keitaro Hara;Kotaro Matsushita;Ippei Harada

  • Affiliations:

  • Department of Information Systems, Tokyo University of Information Sciences, Chiba, Japan 265-8501;Department of Information Systems, Tokyo University of Information Sciences, Chiba, Japan 265-8501;Department of Information Systems, Tokyo University of Information Sciences, Chiba, Japan 265-8501;Department of Environmental Information, Tokyo University of Information Science, Chiba, Japan;Department of Information Systems, Tokyo University of Information Sciences, Chiba, Japan 265-8501;Department of Environmental Information, Tokyo University of Information Science, Chiba, Japan;Department of Environmental Information, Tokyo University of Information Science, Chiba, Japan;Department of Environmental Information, Tokyo University of Information Science, Chiba, Japan

  • Venue:
  • Artificial Life and Robotics
  • Year:
  • 2010

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Abstract

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.