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Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier

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3 Author(s)
M. F. Augusteijn ; Colorado Univ., Colorado Springs, CO, USA ; L. E. Clemens ; K. A. Shaw

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

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:33 ,  Issue: 3 )