A method for object-oriented feature extraction from hyperspectral data-generation of new channels by fusion of data

Extracting significant features is essential for processing and transmission of a vast volume of hyperspectral data. Conventional ways of extracting features are not always satisfactory for this kind of data in terms of optimality and computation time. The authors present an object-oriented feature extraction method designed for supervised classification. After all the data are reduced and orthogonalized, a set of appropriate features for the prescribed purpose is extracted as linear combinations (fused channel) of the reduced components. Each dimension of hyperspectral data is weighted and fused according to the extracted features, which means the generation of new channels from hyperspectral data. Results of feature extraction are applied to evaluating the performance of sensors and to designing a new sensor.