A NOVEL SYSTEM FOR CONTENT BASED RETRIEVAL OF MULTI-LABEL REMOTE SENSING IMAGES

This paper presents a novel content based remote sensing (RS) image retrieval system that consists of: i) a spatial and spectral image description scheme; and ii) a sparsity based supervised retrieval method. Spatial image description is based on the scale invariant feature transform (SIFT), while a novel descriptor defined based on the bag of spectral values is proposed to express spectral features. With the conjunction of these two feature vectors RS image retrieval is instrumented via a sparse reconstruction-based approach. These sparse reconstructions are used to estimate the likelihood of a scene to contain a land-cover class label. Applying this method separately for each land-cover class, one achieves retrieval in the framework of multi-label remote sensing image retrieval. Experimental results obtained on an archive of hyperspectral images show the effectiveness of the proposed system.