17 December 1996 Line extraction from Synthetic Aperture Radar scenes using a Markov random field model
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Due to the speckle effect of coherent imaging the detection of liens in SAR scenes is considerably more difficult than in optical images. A new approach to detect lines in noisy images using a Markov random field model and Bayesian classification is proposed. The unobservable object classes of single pixels are assumed to fulfill the Markov condition, i.e. to depend on the object classes of neighboring pixels only. The influence of neighboring line pixels is formulated based on potentials derived from a random walk model. Locally, the image data is evaluated with a rotating template. As SAR intensity data is deteriorated by multiplicative noise, normalized intensity ratio is used as the response of the local line detector resulting in a constant false alarm rate. The new approach integrates SAR intensity and coherence from interferometric processing of a SAR scene pair. Besides maximum a posterior and iterated conditional modes estimation of the object parameters, an implementation of local highest confidence first estimation is used. It is initially applied to the sites which are most probably structures in object space, and is then allowed to progress to regions less promising for line detection depending on the results of previous iterations. In this way processing times are substantially reduced.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Olaf Hellwich, Olaf Hellwich, } "Line extraction from Synthetic Aperture Radar scenes using a Markov random field model", Proc. SPIE 2958, Microwave Sensing and Synthetic Aperture Radar, (17 December 1996); doi: 10.1117/12.262685; https://doi.org/10.1117/12.262685

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