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Radar (SAR) images often exhibit profound appearance variations due to avariety of factors including clutter noise produced by the coherent nature ofthe illumination. Ultrasound images and infrared images have similar clutteredappearance, that make 1 dimensional structures, as edges and object boundariesdifficult to locate. Structure information is usually extracted in two steps:first, building and edge strength mask classifying pixels as edge points byhypothesis testing, and secondly estimating from that mask, pixel wideconnected edges. With constant false alarm rate (CFAR) edge strength detectorsfor speckle clutter, the image needs to be scanned by a sliding window composedof several differently oriented splitting sub-windows. The accuracy of edgelocation for these ratio detectors depends strongly on the orientation of thesub-windows. In this work we propose to transform the edge strength detectionproblem into a binary segmentation problem in the undecimated wavelet domain,solvable using parallel 1d Hidden Markov Models. For general dependency models,exact estimation of the state map becomes computationally complex, but in ourmodel, exact MAP is feasible. The effectiveness of our approach is demonstratedon simulated noisy real-life natural images with available ground truth, whilethe strength of our output edge map is measured with Pratt's, Baddeley an Kappaproficiency measures. Finally, analysis and experiments on three differenttypes of SAR images, with different polarizations, resolutions and textures,illustrate that the proposed method can detect structure on SAR imageseffectively, providing a very good start point for active contour methods.

Markovian models for one dimensional structure estimation on heavily noisy imagery