Quasi-optimal compression of noisy optical and radar images

It is often necessary to compress remote sensing (RS) data such as optical or radar images. This is needed for transmitting them via communication channels from satellites and/or for storing in databases for later analysis of, for instance, scene temporal changes. Such images are generally corrupted by noise and this factor should be taken into account while selecting a data compression method and its characteristics, in the particular, compression ratio (CR). In opposite to the case of data transmission via communication channel when the channel capacity can be the crucial factor in selecting the CR, in the case of archiving original remote sensing images the CR can be selected using different criteria. The basic requirement could be to provide such a quality of the compressed images that will be appropriate for further use (interpreting) the images after decompression. In this paper we propose a blind approach to quasi-optimal compression of noisy optical and side look aperture radar images. It presumes that noise variance is either known a priori or pre-estimated using the corresponding automatic tools. Then, it is shown that it is possible (in an automatic manner) to set such a CR that produces an efficient noise reduction in the original images same time introducing minimal distortions to remote sensing data at compression stage. For radar images, it is desirable to apply a homomorphic transform before compression and the corresponding inverse transform after decompression. Real life examples confirming the efficiency of the proposed approach are presented.