A level set method for very high resolution airborne sar image segmentation

This paper investigates the segmentation problem for very high resolution airborne synthetic aperture radar (SAR) images. In addition to the instinct speckles, these images show two extra characteristics: scene complexity and intensity inhomogeneity, which make segmentation more difficult. An unsupervised solution is proposed based on level set method. First, a new level set evolution method is put forward, it can get global minimum without initial contour, thus can handle complex images automatically. And the new evolution function also introduces the localizing idea from region-scalable-fitting (RSF) model to deal with the intensity inhomogeneity. Then the two segmentation results for background and targets are fused. The experimental results on real images demonstrate the effectiveness of the proposed method.