Segmentation and compression of SAR imagery via hierarchical stochastic modeling

To abate the enormous costs incurred in the transmission and storage of SAR data, we present a segmentation driven compression technique using hierarchical stochastic modeling within a multiscale framework. Our approach to SAR image compression is unique in that we exploit the multiscale stochastic structure inherent in SAR imagery. This structure is well captured by a set of scale auto-regressive models that accurately characterize the evolution in scale. We thus use the local evolution in scale of SAR imagery to generate a segmentation map which is then used in tandem with the corresponding models to provide a robust, hierarchical compression technique