Target Detection in Sea Clutter with Application to Spaceborne SAR Imaging

TL;DR

This paper develops an adaptive CFAR-based target detection method for sea clutter in spaceborne SAR images, demonstrating that Weibull distribution models background clutter more accurately, leading to improved detection performance.

Contribution

It introduces a Weibull distribution-based statistical model for sea clutter and integrates it into an adaptive CFAR detector for enhanced target detection in SAR imagery.

Findings

Weibull distribution models sea clutter more accurately than other distributions.

The proposed method improves detection performance on RADARSAT-1 data.

Experimental results validate the effectiveness of the Weibull-based CFAR detector.

Abstract

In this paper, the challenging task of target detection in sea clutter is addressed. We analyze the statistical properties of the signals which have been received from the scene and based on that, we model the amplitude of the signals that have been reflected from the background sea clutter according to several well-known probability distribution functions. Next, by exploiting the Kullback-Leibler (KL) divergence metric as a goodness-of-fit test, we will demonstrate that among the proposed probability distributions, the Weibull distribution can model the statistical properties of the background sea clutter with higher accuracy. Subsequently, we utilize the aforementioned information to design an adaptive threshold based on the Constant False Alarm Rate (CFAR) algorithm to detect the energy of the targets which have been buried in the sea clutter. Thorough analysis of the experimental data gathered from the Canadian RADARSAT-1 satellite demonstrates the overall effectiveness of the proposed method.