Abstract
This paper considers an estimation of rice-planted area by using remote sensing data. The classification method is based on a competitive neural network and the remote sensing data are observed by a satellite before and after planting rice in 1999 in Hiroshima, Japan. Three RADAR Satellite (RADARSAT) and one Satellite Pour l'Observation de la Terre(SPOT)/High Resolution Visible (HRV) data are used to extract rice-planted area. Synthetic Aperture Radar (SAR) backscattering intensity in rice-planted area decreases from April to May and increases from May to June. Thus, three RADARSAT images from April to June are used in this study. The Self-Organizing feature Map (SOM) classification was applied the RADARSAT and SPOT to evaluate the rice-planted area estimation. It is shown that theSOM of competitive neural networks is useful for the classification of the satellite data.
