Sar Image Segmentation Based On Level Set Approach And Generalized Gamma Distribution

Synthetic aperture radar (SAR) can be operated day and night under all-weather conditions, with its resolution being independent of distance and having the capability of penetrating the obstacles such as cloud and smoke, thereby it can obtain the surface information over large areas, also plays a decisive role. In virtue of the nature of coherent imaging, the SAR images are inherently susceptible to speckle. This thesis focuses on the segmentation of SAR images, since the generic segmentation methods of optical images are not appropriate to SAR case.As an important method, level set has been widely used in the field of SAR image segmentation due to the ability of handling the change of curve topology structure and fast segmentation speed. In terms of the characteristics of SAR images, the research of level set method mainly lies in two perspectives:one is to first despeckle the SAR images, and then segment the resulting images by using the level sets for optical images; the other is directly to implement the segmentation of SAR images. Because the despeckling step in the former scheme will lose the edge information, here we adopt the latter one, and propose two level-set methods of SAR image segmentation based on Generalized Gamma Distribution.Firstly, a variational level set method is introduced based on the Generalized Gamma Distribution to segment the intensity and amplitude SAR images with homogeneous, heterogeneous, and extremely heterogeneous regions. Specifically, it divides the given SAR image into background and object areas, respectively following the Generalized Gamma Distribution, whose energy function is composed of the regional energy and the energy based on the length of the curve. Among them, the regional energy is designed according to the maximum likelihood criterion. Considering the disadvantages of general level set method, such as unstability, high requirement of step size, and reinitialization, we make use of a variational level set method to describe the energy function, and then minimize the energy functional with variational approach to solve the evolution equation for the purpose of segmentation. The experimental results verify the validity of the proposed method on synthetic and actual SAR images.Secondly, to address the issue of slow segmentation speed in the above method, we present a new level set method for SAR image segmentation from another perspective, for which the parameters of Generalized Gamma Distribution are used to design the energy function. With the parameter estimates for each pixel in image region, the cumulative distribution function is derived as the related energy function in the level set evolution by the criterion of maximizing the regional mean energy. The final level set stage achieves S AR image segmentation according to the energy bands. The experimental results demonstrate the effectiveness of this proposed method on synthetic and actual SAR images.