SAR Image Segmentation Based On The Active Contour Model

Image segmentation is a fundamental problem for SAR image processing and also one of crucial steps that heavily influences the performance of SAR image automatic interpretation. Because of the limitation of extracting only local information with disconnected boundary of the segmented region, and lack of ability to integrate prior knowledge about the segmented objects, classical image segmentation techniques cannot satisfy the requirement of complex SAR image segmentation applications. In this case, a flexible framework is required that can integrate both low vision information from images and prior knowledge about target objects seamlessly for a consistent representation of the segmentation of the segmented regions. The active contour model based image segmentation techniques just meet this requirement. However, current active contour models are usually proposed for noncoherent images, thus they cannot correctly model the edge-based and region-based information for coherent SAR images, which limits their applications to SAR images. Moreover, there exit some reasons for improving and extending current models further. First, they are inefficient for most images. Second, they are sensitive to initial conditions. Finally, they are prone to trapping into local minima.To address these problems, this thesis presented some studies concentrated in three topics:(1) Based on SAR image edge detectors, two novel geodesic active contour models, the ROEWA-GAC model and efficient ROEWA-GAC model, are proposed. The basic idea is that we use an edge indicator function based on the ROEWA operator to replace the original edge indicator function based on gradients. Thus, the ability of detecting edges and the accuracy of locating edges are increased, which make the models more appropriate for SAR image segmentation. In addition, a"balloon force"term is added to the original model's energy functional in order to enhance the power for curve evolution. As a result, the speed of curve evolution increases and the sensitivity to the initial contour is reduced. Besides, we use two different ways to improve the speed of the models. For the ROEWA-GAC model, an unconditionally stable AOS difference scheme and a fast algorithm for re-initialization of the level set function are adopted, which not only enhance the model's stability, but also speed up the model's convergence. For the efficient ROEWA-GAC model, a term penalizing the level set function is added to the energy functional in order to force the level set function to be close to a signed distance function and therefore completely eliminates the need of the costly re-initialization procedure. Thanks to the contribution of this term, the numerical calculation of the model can be implemented by a simple explicit difference scheme; at the same time the evolution speed keeps very fast.(2) Based on the MAP criterion and variational level set methods, the author proposes a general statistical active contour model and gives a thorough discussion on the theoretical solutions, numerical calculation schemes and segmentation algorithms based on it. The model has several advantages. First, the concrete formulation of the statistical distribution is not assumed in advance. An appropriate distribution is assigned to handle a concrete segmentation problem. Thus it makes our model suitable for various kinds of segmentation problem. This paper adopts the G 0 distribution to fit the SAR image, which overcomes the shortcomings that the distribution used in the current model does not have the good fitting ability for SAR images. As a result, the segmentation accuracy increases. Second, the model uses the regional statistical information to locate the boundaries, which makes it very suitable for SAR edge detection. Third, a level set function penalizing term is added, which makes sure the level set function as a signed distance function. Consequently, the exhaustive reinitialization steps are eliminated and the sensitivity to the initial contours is reduced. Finally, a semi-implicit difference scheme is used to make the numerical calculation unconditionally stable.(3) Based on the geodesic active contour model and statistical active contour model, the author proposes a novel active contour model with global minimizers and gives a thorough discussion on its theoretical solution, numerical calculation scheme and the corresponding segmentation algorithm. The novelties of the model are as follows. First, the model uses both the edge and region terms to locate object boundaries, which further favors the accurate image segmentation. Second, the edge term based on SAR image edge detectors attracts the contour to the actual image edges. Third, the region term based on the G 0 distribution enhances the fitting ability for SAR images. Finally, a fast global algorithm based on the dual formulation of the total variation is introduced to strengthen the practicability of the model. The experimental results on the simulated images and real MSTAR, ERS, Radasat and NASA/JPL AIRSAR data show that the segmentation algorithm based on the proposed model has several advantages. First, it produces accurate segmented region boundaries. Second, the segmented regions are homogeneous. Third, the computational efficiency is very high and no post-processing steps are needed. Finally, the algorithm is not sensitive to the setup of parameters and robust to initial conditions.