Extracting line features from synthetic aperture radar (SAR) scenes using a Markov random field model

Due to the speckle effect of coherent imaging the detection of lines 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 (MRF) 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, the response of the local line detector is a normalized intensity ratio which results in a constant false alarm rate. The approach integrates intensity, coherence from interferometric processing of a SAR scene pair, and given Geographic Information System (GIS) data.