OPTIMAL HYPERSPECTRAL CLASSIFICATION FOR PADDY FIELD WITH SEMISUPERVISED SELF-LEARNING

Monitoring and management of paddy fields are one of key elements for not only stable production but also ensuring national food security. Classification of growth stage with remote sensing data is expected to be a highly effective solution, which can capture large area in one time observation. In general cases, a pixel-based classification is one of the most attractive choices. However, acquiring enough number of field survey plots for the classification is not easy from the aspect of consumed time and cost. This problem can impact negatively on the accuracy of classification map. In this paper, we propose semisupervised classification method considering characteristic of paddy field in order to provide an optimal classification map with hyperspectral data.