Publication

Abstract : Due to the dramatically increased volume of remote sensing (RS) image archives, images are usually stored in a compressed format to reduce the storage size. Existing content-based RS image retrieval (CBIR) systems require as input fully decoded images, thus resulting in a computationally demanding task in the case of large-scale CBIR problems. To overcome this limitation, in this article, we present a novel CBIR system that achieves a coarse-to-fine progressive RS image description and retrieval in the partially decoded Joint Photographic Experts Group (JPEG) 2000 compressed domain. The proposed system initially: 1) decodes the code blocks associated only to the coarse wavelet resolution and 2) discards the most irrelevant images to the query image based on the similarities computed on the coarse resolution wavelet features of the query and archive images. Then, the code blocks associated with the subsequent resolution of the remaining images are decoded and the most irrelevant images are discarded by computing similarities considering the image features associated with both resolutions. This is achieved by using the pyramid match kernel similarity measure that assigns higher weights to the features associated with the finer wavelet resolution than to those related to the coarse wavelet resolution. These processes are iterated until the codestreams associated with the highest wavelet resolution are decoded. Then, the final retrieval is performed on a very small set of completely decoded images. Experimental results obtained on two benchmark archives of aerial images point out that the proposed system is much faster while providing a similar retrieval accuracy than the standard CBIR systems.