Partitioning of Hyperspectral Images in Main Categories and Fine Classes

An unsupervised approach to automatically and objectively detect all classes in hyperspectral images based on physical characteristics provided by the sensors is proposed. The partitioning is done in two steps: first, the main classes are detected; then, each main class is subdivided into fine classes. The different evaluations of the proposed method on hyperspectral images show the relevance and the coherence of the partitioning results obtained in both steps.