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This paper considers edge interval estimation between two regions of a synthetic aperture radar (SAR) image, which differ in texture. This is a difficult task because SAR images are contaminated with speckle noise. Different point estimation strategies under multiplicative noise are discussed in the literature. It is important to assess the quality of such point estimates and to also perform inference under a given confidence level. This can be achieved through interval parameter estimation. To that end, we propose bootstrap-based edge confidence interval. The relative merits of the different inference strategies are compared using Monte Carlo simulation. The results show that interval edge estimation can be used to assess the accuracy of an edge point estimate. They also show that interval estimates can be quite accurate and that they can indicate the absence of an edge. In order to illustrate interval edge estimation, we also analyze a real data set. [ABSTRACT FROM PUBLISHER]

For many multidimensional applications of synthetic aperture radar (SAR) imaging, the estimation of the covariance matrix for each resolution cell is a critical processing step. The context of this work is the application of covariance matrix estimation for multi-baseline interferometric SAR data sets. In order to ensure local stationarity, which is needed for an unbiased estimation, adaptive techniques are necessary. In this paper, a new approach for adaptive covariance matrix estimation is proposed and evaluated based on measures known from the field of image processing. The procedure is centered around the idea of checking whether the neighboring pixels belong to the same statistical distribution as the currently investigated pixel by applying a threshold to the respective probability density function. All inlier pixels are then used to estimate the complex covariance matrix of the reference pixel. From this covariance matrix, both amplitude and interferometric phase values are extracted, which are then combined for all pixels in the stack in order to employ techniques for the evaluation of filtering efficiency that are typically used in image denoising research. It is found that the proposed algorithm provides high filtering efficiency and good detail preservation at the same time. Apart from that, it is found to be particularly suitable for small-sized stacks of coregistered SAR imagery. [ABSTRACT FROM PUBLISHER]