Deriving texture feature set for content-based retrieval of satellite image database

In this paper, the performance of similarity retrieval from satellite image databases by using different sets of spatial and transformed-based texture features is evaluated and compared. A benchmark consisting of 37 satellite image clips from various satellite instruments is devised for the experiments. We show that although the proposed feature set perform only slightly better with the Brodatz set, its performance is far superior for the satellite images. The results indicates that more than 25% of the benchmark patterns can be retrieved with more than 80% accuracy by using normalized Euclidean distance. In contrast, less than 10% of the patterns are retrieved with more than 80% accuracy by using transformed-based feature sets (such as those based on Gabor filter or Quadrature Mirror Filter (QMF)).