Estimation of rice-planted area using competitive neural network

This paper considers a classification of estimation of rice planted area by using remote sensing data. The classification method is based on a competitive neural network and the sattelite data are remote sensing data observed 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 estimate rice-planted area. Synthetic Aperture Radar (SAR) back-scattering 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 SOM classification was applied the RADARSAT and SPOT to evaluate the rice-planted area estimation. It is shown that the Self-Organizing feature Map (SOM) of competitive neural networks is useful for the classification of the satellite data by SAR to estimate the rice planted area.