On the framework, algorithms and applications of hyperspectral remote sensing data mining

Based on the analysis to DM and hyperspectral RS information processing, it is pointed out that hyperspectral RS data mining will promote the development of intelligent information processing. The framework and some key techniques are discussed in detail. The knowledge that can be discovered from hyperspectral RS information includes: spectral signatures of ground objects; spatial characteristics, rules and relationships; knowledge about genesis, characterization and diagnosis; relationship among different bands and spectral knowledge; dynamic evolution knowledge and abnormity and isolated point identification. By analysis and experiments, some algorithms proved effective to HRSDM including association rule mining, clustering and artificial neural network (ANN), rough set and fuzzy theory, decision tree and data cube are analyzed. Finally, some potential applications of HRSDM including typical information extraction and identification, quantitative RS and RS inversion, image classification and mixed pixel decomposition, and feature extraction and selection of optimal band combination are discussed.