In this paper, we describe an extension of an automatic road extractionprocedure developed for single SAR images towards multi-aspect SARimages. Extracted information from multi-aspect SAR images is notonly redundant and complementary, in some cases even contradictory.Hence, multi-aspect SAR images require a careful selection withinthe fusion step. In this work, a fusion step based on probabilitytheory is proposed. During fusion each extracted line primitive isassessed by means of Bayesian probability theory. The assessmentis based on the attributes of the line primitive (i.e. length, straightness,etc), global context and sensor geometry. The fusion and its integrationinto the road extraction system are tested in a sub-urban SAR scene.     «

In this paper, we describe an extension of an automatic road extractionprocedure developed for single SAR images towards multi-aspect SARimages. Extracted information from multi-aspect SAR images is notonly redundant and complementary, in some cases even contradictory.Hence, multi-aspect SAR images require a careful selection withinthe fusion step. In this work, a fusion step based on probabilitytheory is proposed. During fusion each extracted line primitive isassessed by means of Bayesian proba...     »