Extraction of rice-planted area using a self-organizing feature map

We introduce a neural network of self-organizing feature map (SOM) to classify remote-sensing data, including microwave and optical sensors, for the estimation of areas of planted rice. This method is an unsupervised neural network which has the capability of nonlinear discrimination, and the classification function is determined by learning. The satellite data are observed before and after rice planting in 1999. Three sets of RADARSAT and one set of SPOT/HRV data were used in Higashi-Hiroshima, Japan. The RADARSAT image has only one band of data and it is difficult to extract the rice-planted area. However, the SAR back-scattering intensity in a rice-planted area decreases from April to May and increases from May to June. Therefore, three RADARSAT images from April to June were used in this study. The SOM classification was applied the RADARSAT and SPOT data to evaluate the rice-planted area estimation. It is shown that the SOM is useful for the classification of satellite data.