ミクセルの存在とトレーニングデータの代表性を考慮した時系列低空間分解能画像を用いた土地被覆分類手法

Abstract

This paper describes a land cover classification method, which uses time-series and low spatial resolution satellite images, and which can discriminate the feature space with high reliability for classification from the other. The method decomposes a whole of image histogram into each component, feature distributions of pure classes and mixel class. The decomposition result gives the likelihood function for a specific land cover class, then the feature space is discriminated based on the likelihood function. Validation demonstrates that the presented method is effective to improve the representativeness of training data sets.