Segmentation method for synthetic aperture sonar image using improved region-scalable fitting model

A segmentation method using Region-Scalable Fitting(RSF) model with adaptive scale of kernel function was proposed in order to segment synthetic aperture sonar image automatically and precisely.An automatic initialization method based on K-means,which can reduce human intervention,was brought out to initialize level set functions.Then an improved RSF model with adaptive scale of kernel function was proposed.By utilizing the general rule of sonar imaging that the target and its shadow have approximately the same length in the along-track direction,the model can select kernel function's scale parameter automatically so that it can have two level set functions corresponding to target area and shadow area evolving synchronously,thus increasing the accuracy of segmentation.Experimental results demonstrate that the proposed method can acquire precise segmentation of target and shadow such that it has certain accuracy and adaptability.