Effect of noise on order parameter estimation for K-distributed clutter

The paper addresses the characterisation of high resolution radar image textures in the presence of additive noise, which is inevitably present in the system. Two possible goals are analysed. In the first, the authors consider absolute texture description and identify the extent to which noise degrades performance by introducing a bias. The second is concerned only with segmenting the texture into locally different regions and discusses the effect of the noise on the sensitivity of the measure to texture changes, described in terms of relative variance. Initially, the authors demonstrate that estimates of the mean, normalised log and contrast of the intensity approximate a sufficient statistic for K-distributed clutter. They then compare the performance of a variety of texture measures in terms of the bias in the estimated order parameter for absolute classification and the relative variance for texture segmentation. A normalised log measure is shown to have the best sensitivity overall. However, with additive noise an amplitude contrast measure yields a much smaller classification error with only slightly reduced sensitivity