Control systems principles applied to speckle filtering and geophysical information extraction in multi-channel SAR images

Two new Bayesian Maximum A Posteriori (MAP) vector speckle filters are developed for multi-channel detected Synthetic Aperture Radar (SAR) images. These filters incorporate statistical descriptions of the scene and of the speckle in multichannel SAR images. These models account for the scene and system effects which result in the presence of a certain amount of correlation between the different channels. In order to account for the effects due to the spatial correlation of both the speckle and the scene in SAR images, estimators originating from the local autocorrelation functions (ACF) are incorporated to these filters, to refine the evaluation of the non-stationary first order local statistics, to improve the restoration of the scene textural properties, and to preserve the useful spatial resolution in the speckle filtered image. Since the new established Bayesian speckle filters present the structure of control systems, their application is the first processing step of application-oriented control systems designed to exploit the synergy of SAR sensors. We present here such a control system, designed to retrieve soil roughness and soil moisture through Bayesian ERS/RADARSAT data fusion. Results obtained on a couple of ERS PRI and RADARSAT Standard Beam SAR images show that the new speckle filters present convincing performances for speckle reduction, for texture preservation and for small scene objects detection. The retrieval of soil roughness and soil moisture through Bayesian data fusion of ERS and RADARSAT data provides also valuable results for the monitoring of agriculture and environment.