PERFORMANCE EVALUATION OF TEXTURE MEASURES FOR GROUND COVER IDENTIFICATION IN SATELLITE IMAGES BY MEANS OF A NEURAL-NETWORK CLASSIFIER

The performance of several feature extraction methods for classifying ground covers in satellite images is compared. Ground covers are viewed as texture of the image, Texture measures considered are: cooccurrence matrices, gray-level differences, texture-tone analysis, features derived from the Fourier spectrum, and Gabor filters, Some Fourier features and some Gabor filters were found to be good choices, in particular when a single frequency band was used for classification, A Thematic Mapper(TM) satellite image showing a variety of vegetations in central Colorado was used for the comparison. A related goal was to investigate the feasibility of extracting the main ground covers from an image, These ground covers may then form an index into a database, This would allow the retrieval of a set of images which are similar in contents, The results obtained in the indexing experiments are encouraging.