ETVOS: An Enhanced Total Variation Optimization Segmentation Approach for SAR Sea-Ice Image Segmentation

This paper presents a novel enhanced total variation optimization segmentation (ETVOS) approach consisting of two phases to segmentation of various sea-ice types. In the total variation optimization phase, the Rudin-Osher-Fatemi total variation model was modified and implemented iteratively to estimate the piecewise constant state from a nonpiecewise constant state (the original noisy imagery) by minimizing the total variation constraints. In the finite mixture model classification phase, based on the pixel distribution, an expectation maximization method was performed to estimate the final class likelihood using a Gaussian mixture model. Then, a maximum likelihood classification technique was utilized to estimate the final class of each pixel that appeared in the product of the total variation optimization phase. The proposed method was tested on a synthetic image and various subsets of RADARSAT-2 imagery, and the results were compared with other well-established approaches. With the advantage of a short processing time, the visual inspection and quantitative analysis of segmentation results confirm the superiority of the proposed ETVOS method over other existing methods.