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2012-04-03

Variational SAR Image Segmentation Based on the G0 Model and an Augmented Lagrangian Method

By Jilan Feng, Zongjie Cao, and Yiming Pi
Progress In Electromagnetics Research B, Vol. 39, 373-392, 2012
doi:10.2528/PIERB12011212

Abstract

This paper present a fast algorithm for synthetic aperture radar (SAR) image segmentation based on the augmented Lagrangian method (ALM). The proposed approach considers the segmentation of SAR images as an energy minimization problem in a variational framework. The energy functional is formulated based on the statistical characteristic of SAR images. The total variation regularization is used to impose the smoothness constraint of the segmentation result. To solve the optimization problem efficiently, the energy functional is firstly modified to be convex and differentiable by using convex relaxing and variable splitting techniques, and then the constrained optimization problem is converted to an unconstrained one by using the ALM. Finally the energy is minimized with an iterative minimization algorithm. The effectiveness of the proposed algorithm is validated by experiments on both synthetic and real SAR images.

Citation


Jilan Feng, Zongjie Cao, and Yiming Pi, "Variational SAR Image Segmentation Based on the G0 Model and an Augmented Lagrangian Method," Progress In Electromagnetics Research B, Vol. 39, 373-392, 2012.
doi:10.2528/PIERB12011212
http://jpier.org/PIERB/pier.php?paper=12011212

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