Manifold Adaptation for Constant False Alarm Rate Ship Detection in South African Oceans

Abstract

The detection of ships at sea is a difficult task made more so by uncooperative ships, especially when using transponder-based ship detection systems. Synthetic aperture radar (SAR) imagery provides a means of observation independent of the ships cooperation, and over the years, a vast amount of research has gone into the detection of ships using this imagery. One of the most common methods used for ship detection in SAR imagery is the cell-averaging constant false alarm rate (CA-CFAR) prescreening method. It uses a scalar threshold value to determine how bright a pixel needs to be in order to be classified as a ship, and thus inversely how many false alarms are permitted. This paper presents by a method of converting the scalar threshold into a threshold manifold. The manifold is adjusted using a simulated annealing (SA) algorithm to optimally fit to information provided by the ship distribution map, which is generated from transponder data. By carefully selecting the input solution and threshold boundaries, much of the computational inefficiencies usually associated with SA can be avoided. The proposed method was tested on six ASAR images against five other methods and had a reported detection accuracy (DA) of 85.2% with a corresponding FAR of $\mathbf{1.01} \times \mathbf{10}^{\mathbf{-7}}$ .