Detection of Rice Field Using the Self-organizing Feature Map

  • Sigeru OmatuEmail author
  • Mitsuaki Yano
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)


We consider a detection method of rice field under rainy conditions by using remote sensing data. The classification method is to use a competitive neural network of self-organizing feature map (SOM) by using remote sensing data observed before and after planting rice in Hiroshima, Japan. Three RADAR Satellites (RADARSAT) and one Satellite Pour l’Observation de la Terre(SPOT)/High Resolution Visible (HRV) data are used to detect rice field. Synthetic Aperture Radar (SAR) reflects back-scattering intensity in rice fields. The intensity decreases from April to May and increases from May to June. It is shown that the competitive neural network of self-organizing feature map is useful for the classification of the SAR data to find the area of rice fields.


remote sensing synthetic aperture radar area of rice fields 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Osaka Institute of TechnologyAsahi-kuJapan

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