Comparing SIFT descriptors and gabor texture features for classification of remote sensed imagery

A richer set of land-cover classes are observable in satellite imagery than ever before due to the increased sub-meter resolution. Individual objects, such as cars and houses, are now recognizable. This work considers a new category of image descriptors based on local measures of saliency for labelling land-cover classes characterized by identifiable objects. These descriptors have been successfully applied to object recognition in standard (non-remote sensed) imagery. We show they perform comparably to state-of-the-art texture descriptors for classifying complex land-cover classes in high- resolution satellite imagery while being approximately an order of magnitude faster to compute. This speedup makes them attractive for realtime applications. To the best of our knowledge, this is the first time this new category of descriptors has been applied to the classification of remote sensed imagery.