Retrieval of High Resolution Satellite Images Based on Steerable Pyramids
Samia Bouteldja, Assia Kourgli · 2015
In this paper we propose a new rotation and scale invariant representation for high resolution satellite image retrieval based on Steerable Pyramid Decomposition. By calculating the statistical measures of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation and scale invariance, the feature vectors are circularly shifted until obtaining the minimum possible distance. Experiments were conducted using 8 image classes from land-use/land-cover (LULC) UCMerced dataset. Results obtained are compared with color Gabor opponent texture features. Tests and evaluation measures demonstrate that the proposed technique gives a good performance in terms of high precision.