A novel active contour model based on modified symmetric cross entropy for remote sensing river image segmentation

The traditional active contour models cannot segment the remote sensing river images accurately. To solve this problem, a novel active contour model based on modified symmetric cross entropy is proposed. In the proposed model, the external energy constraint terms are defined by the symmetric cross entropy and the region fitting centers are represented by the medians of pixel grayscale values inside the object and background regions. Moreover, the penalty energy term is incorporated into the energy functional to avoid the re-initialization. In order to improve the segmentation efficiency of the proposed model, the Chebyshev distance between the pixel grayscale values inside the region and its region fitting center is chosen as its region energy weight, which can be adaptively adjusted, instead of the constant region energy weight. The extensive experiments are performed on a large number of remote sensing river images and the results demonstrate that, compared with the CV model, the GAC model, the CEACM model, the RSF model, the LIF model, and the LGIF model, the proposed model can segment the images more accurately and rapidly, which has the clear advantages in both segmentation performance and segmentation efficiency.