Road extraction for SAR imagery based on the combination of beamlet and a selected kernel

In this paper, an algorithm applied for road extraction on SAR image is proposed, which is based on a multi-scale linear feature detector and beamlet framework, and then a quadratic kernel is introduced to offer optimal representation for the circle roads, aiming at improving the extraction quality. Firstly, a multi-scale pyramid is built on the input image and at each level the image is subdivided into a series of dyadic squares that constructs a quadtree. Then the multi-scale linear feature detector and beamlet are employed to compute pixels' responses. Finally, a quadratic kernel for non-linear candidates is introduced and adaptively selects the generating direction of segments. Experiments on TerraSAR images prove that the proposed approach significantly improves the extraction quality and performance when compared to several methods.