On a Novel Approach Using MLCC and CFAR for the Improvement of Ship Detection by Synthetic Aperture Radar

Abstract

Multilook cross correlation (MLCC) is a useful technique in extracting the images of ships embedded in heavy sea clutter by synthetic aperture radar (SAR). In the ship detection experiment in 2006 by Phased Array L-band Synthetic Aperture Radar (PALSAR) on board the Advanced Land Observing Satellite, we applied MLCC to PALSAR data in order to extract small fishing boats. The result was that some boats were detected by thresholding MLCC coherence images under favorable conditions. However, it was also found that the threshold method was not suitable to automatically determine the threshold levels corresponding to the desired false alarm rate (FAR) values. In order to overcome this problem and to improve the accuracy of ship detection by MLCC, we propose a new and simple technique of MLCC-constant FAR (CFAR) or gamma-CFAR. In this method, CFAR is applied to interlook coherence images produced by MLCC. We tested this method using simulation and PALSAR data and then found out substantial improvement in signal-to-noise ratio and FAR in comparison with the coherence image alone. In this letter, we summarize the MLCC-CFAR algorithm and the experimental results.