SAR image segmentation algorithms based on I-divergence-TV model

TL;DR

This paper introduces a new variational active contour model based on I-divergence-TV for effective SAR image segmentation, especially in noisy conditions, using a fast fixed point algorithm for improved robustness and efficiency.

Contribution

It presents a novel hybrid edge-region based active contour model incorporating I-divergence-TV and a fast fixed point algorithm for SAR image segmentation.

Findings

Robust segmentation of SAR images with multiplicative gamma noise.

Efficient convergence compared to existing methods.

Effective detection of weak or blurred edges.

Abstract

In this paper, we propose a novel variational active contour model based on I-divergence-TV model to segment Synthetic aperture radar (SAR) images with multiplicative gamma noise, which hybrides edge-based model with region-based model. The proposed model can efficiently stop the contours at weak or blurred edges, and can automatically detect the exterior and interior boundaries of images. We incorporate the global convex segmentation method and split Bregman technique into the proposed model, and propose a fast fixed point algorithm to solve the global convex segmentation question[25]. [25] is submitted on 29-Aug-2013, and our early edition ever submitted to TGRS on 12-Jun-2012, Venkatakrishnan et al. [26] proposed their 'pnp algorithm' on 29-May-2013, so Venkatakrishnan and we proposed the 'pnp algorithm' almost simultaneously. Experimental results for synthetic images and real SAR images show that the proposed fast fixed point algorithm is robust and efficient compared with the state-of-the-art approach.