MSTAR image segmentation with multi-phase level set based on probability density model
Xiaojin Hou, Lin Yan, Shuang Wang, Biao Hou · 2014
Radar image segmentation is a fundamental problem in radar image interpretation. Radar images often contain a great deal of noise. Level set method, known as deformable model, is a powerful image segmentation technique. It can get accurate contours of clear-cut objects in image without noise, but has poor performance in getting contours of objects in a noisy image. In this paper, a new multi-phase level set based on probability density model is proposed. We use histogram, a non-parametric density estimation method, to describe the statistical information of each pixel in its neighborhood and the pixels in each subset in the image. The comparability between them computed by inner product function is used as the curve energy in multi-phase level set method. The statistical information is incorporated into the multi-phase level set framework, which can cope with the influence of noise on image segmentation. This new method is particularly well adapted to detection of objects of interesting in a noisy image. We illustrated the performance of the new method on MSTAR images. The experimental results show that incorporating statistical information into the multi-phase level set framework, consistent objects are obtained, and accurate and robust segmentations can be achieved.