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A neural network of Self-Organizing feature Map (SOM) to classify remote sensing data including microwave and optical sensors for the estimation of rice-planted area is considered. The method is an unsupervised neural network and has capability of nonlinear discrimination and the classification function is determined by learning. The satellite data are observed before and after planting rice in 1999...
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)...
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...
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