Finding structures in ratio images

Abstract

Synthetic Aperture Radar (SAR) imaging play a central role in Remote Sensing applications due to, among other important features, its ability to provide high-resolution, day-and-night and almost weather-independent images. SAR images are affected from a granular contamination, speckle noise, that can be described by a multiplicative model. Many despeckling techniques have been proposed in the literature, as well as measure of the quality of the results they provide. Speckle filters provide (X) over cap, estimator of the true image X, based solely on the observed data Z, then an ideal estimator would be the one for which the ratio of the observed image to the filtered one Pi =Z/(X) over cap is only speckle. The quality of the filter can be, then, assessed by its closeness to this hypothesis. We tackle the problem of quantitatively measuring the quality of speckle filters by the criterion of lack of structure in the ratio image they produce. We propose the use of Haralick's textural features for the identification of remaining structures in ratio images. In order to do so, we first analyze the distribution of such features under the null hypothesis (H-0) of absence of structure; then we sample from ratio images with barely visible remaining structures.