Synthetic aperture radar river image segmentation using improved localizing region-based active contour model

摘要

Adaptive localizing region-based active contour model driven by Laplacian kernel-based fitting energy is proposed for improving the efficiency and accuracy of synthetic aperture radar (SAR) river image segmentation in the paper. Defining regional energy functional that depends on the Laplacian kernel distance which is robust and non-Euclidean, Laplacian kernel distance is nonlinear transformation, whosetransformed space can be linear classification. Additionally, providing the novel calculation for fitting center which relies on the local and global gray value, furthermore, the adaptive selection function of local radius is made. By using both of them, the proposed model can improve the accuracy of the fitting center and local region; afterward, the evolution of the curve can achieve the global optimal and be controlled better. Finally, in order to speedup the computation of proposed model, the localized region surrounded by adjacent four pixel points on the evolution curve can be replaced by the localized region of the intermediate pixel. The proposed model has been successfully applied to river channel extraction from synthetic aperture radar (SAR) images with desirable results. Comparisons with other state-of-the-art approaches demonstrate the great performances of the model.