A Distribution Independent Ship Detector for PolSAR Images

Abstract

Ship detection with Polarimetric Synthetic Aperture Radar (PolSAR) images has attracted a lot of attention in recent years. However, modeling the distribution of clutter is a complicated task. This paper introduces a distribution independent ship detector for PolSAR images. Firstly, in order to improve the detection performance, the multichannel PolSAR data are projected onto a one-dimensional space utilizing an adaptive linear filter. The design of linear filter is modeled as a nonconvex optimization problem with the principle of maximization Target to Clutter Ratio (TCR), which is solved by an iteration optimization algorithm. Then, the convergence and computational complexities of the proposed algorithm are theoretically analyzed. After that, a distribution independent detector with a bounded Constant False Alarm Rate (CFAR) property is proposed to distinguish ships from sea clutter. The detection threshold is calculated based on the Markov inequality without modeling the statistical distribution of clutter. Experiments are carried out on real Radarsat-2 and AirSAR data to test the proposed detector. The results demonstrate that the proposed detector which takes the distribution independent and unsupervised properties as the main advantages, also achieves comparable detection performance with state-of-the-art methods. Moreover, additional experiments verify the robustness of the proposed detector to the initialization of the algorithm, even though the optimization problem is non-convex. Finally, the effects on detection results caused by polarization characteristics are investigated to give a further explanation about linear polarization enhancement.

Topological Optimization Method for Ship Detection in Sar Images

“…Subsequent works mainly focus on these aspects to design effective detection algorithms [9]. Over the years, many window structures and statistical models have been designed in the CFAR framework to deal with clutter and complex background distributions [10]. However, with the increasing complexity of the model, parameter estimation becomes a difficult problem and even limits the real application of CFAR technology.…”