ABSTRACT
This study investigates the application of the level set method for automated multi-phase segmentation of multi-band and polarimetric synthetic aperture radar (SAR) images. The level set formulation is used to form an energy functional that includes the image statistical information defined on active contours. In addition to the classical Wishart/Gaussian distribution for locating region boundaries, edge information is incorporated into the energy functional to improve the performance of polarimetric data segmentation. An active contour model with an edge indicator is proposed by assuming that the image boundary term follows a Gibbs prior. An empirical parameter setting criterion is developed to ensure that the components of the energy function are in proper proportion. We then investigate the multi-phase extension for energy minimization, and use a piecewise constant model to embed the proposed active contour model. Synthetic and real multi-band polarimetric SAR data are used for verification. The experiments show that our method is superior to another level set method based on the Wishart/Gaussian distribution, in which SAR edge information is not included, particularly for discriminating among low-contrast regions. Furthermore, results show that segmentation is improved when multi-band data are used in the level set framework.
