FEATURE SELECTION FOR SATELLITE IMAGE INDEXING

Lots of feature extraction procedures can be found in the literature. We propose to automatically select the best ones using automatic supervised feature selection algorithms. We demonstrate the efficiency of such a methodology in comparing texture features sets (using Haralick coefficients, Gaussian Markov Random Fields, several wavelets,...) computed on satellite images. We illustrate the fact that geometrical features and texture features have to be combined to enhance the discriminative power of the selected features set.

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