ON THE MODEL-BASED ESTIMATION OF BACKSCATTER TEXTURE FROM SAR IMAGE TEXTURE FOR AREA-EXTENSIVE SCENES

This study addresses the discrimination and characterization of sea-ic e scenes using synthetic aperture radar (SAR) image texture. We develo p a model for a second-order statistic, the autocorrelation function. This model is based on a phenomenological model of the radar scatterin g from sea ice in a marginal ice zone (MIZ) and a model of the SAR ima ging system suitable for airborne geometries. These models are based o n our understanding of the scene and system and become implicit assump tions in the subsequent texture analysis. In order to examine the vali dity of these assumptions we have developed an experimental methodolog y which is useful when doing texture analysis outside the domain of se a-ice scenes and airborne SAR geometries. This methodology involves tw o sets of experiments focused on the scene and system models. The firs t set of experiments demonstrated that the simple second-order SAR ima ging model was appropriate for the airborne imaging geometry used in t he collection of the SAR data. It was further shown that the sea ice r esponds to the SAR as diffuse targets and that the statistics of image ry with large processed bandwidths are very sensitive to errors in sys tem focus. The second set of experiments pointed out the difficulty in testing for fully developed speckle with SAR image data. These experi ments also demonstrated that several of the ice types did indeed respo nd to the SAR as non-Gaussian targets. A small percentage of pixels in the complex image data appeared to have correlated in-phase and quadr ature components. However, the evidence allowing us to infer that part ly developed speckle is the cause of these correlations is weak and am biguous.