A novel system for content-based retrieval of single and multi-label high-dimensional remote sensing images

This paper presents a novel content-based remote sensing (RS) image retrieval system that consists of the following. First, an image description method that characterizes both spatial and spectral information content of RS images. Second, a supervised retrieval method that efficiently models and exploits the sparsity of RS image descriptors. The proposed image description method characterizes the spectral content by three different novel spectral descriptors that are: raw pixel values, simple bag of spectral values and the extended bag of spectral values descriptors. To model the spatial content of RS images, we consider the well-known scale invariant feature transform-based bag of visual words approach. With the conjunction of the spatial and the spectral descriptors, RS image retrieval is achieved by a novel sparse reconstruction-based RS image retrieval method. The proposed method considers a novel measure of label likelihood in the framework of sparse reconstruction-based classifiers and generalizes the original sparse classifier to the case both single-label and multi-label RS image retrieval problems. Finally, to enhance retrieval performance, we introduce a strategy to exploit the sensitivity of the sparse reconstruction-based method to different dictionary words. Experimental results obtained on two benchmark archives show the effectiveness of the proposed system.