Abstract
When applying the constant false alarm rate (CFAR) detector to ship detection on synthetic aperture radar (SAR) imagery, multiple interferers such as upwelling, breaking waves, ambiguities, and neighboring ships in a dense traffic area will degrade the probability of detection. In this paper, we propose a novel variable index and excision CFAR (VIE-CFAR) based ship detection method to alleviate the masking effect of multiple interferers. Firstly, we improve the variable index (VI) CFAR with an excision procedure, which censors the multiple interferers from the reference cells. And then, the paper integrates the novel CFAR concept into a ship detection scheme on SAR imagery, which adopts the VIE-CFAR to screen reference cells and the distribution to derive detection threshold. Finally, we analyze the performances of the VIE-CFAR under different environments and validate the proposed method on both ENVISAT and TerraSAR-X SAR data. The results demonstrate that the proposed method outperforms other existing detectors, especially in the presence of multiple interferers.
Highlights
Ship detection filters the peaks among sea clutter to indicate the location of potential ships
We compare the probability of detection (PD) and the probability of false alarm (PFA) performances of the VIE-constant false alarm rate (CFAR) with the existing CFAR detectors, including the cell averaging (CA), GO, SO, order statistic (OS) (k = 21, k is the order of the cell being taken as the estimation of local statistic), and variability index (VI)-CFAR
As described in [14], the values of VI and mean ratio (MR) are related to the clutter to noise ratio (CNR) and interferer to noise ratio (INR) of the variable environments, as well as the number of reference cells
Summary
Ship detection filters the peaks among sea clutter to indicate the location of potential ships. Besides the methods based on wavelet analysis [8] and subaperture correlation [9], constant false alarm rate (CFAR) method is the most popular technique for ship detection on SAR imagery [3]. The nonhomogeneous environments result in either an excessive increase in false alarms or degradation of detection probability. To overcome these problems, alternative CFAR processors are proposed. The great-of (GO), smallest-of (SO), order statistic (OS), and trimmed mean (TM) CFARs are the most representative ones. These CFAR detectors produce diverse performances under different clutter environments [11]. The VI-CFAR provides low CFAR loss in homogeneous environments and performs robustly in the presence of clutter edges and properly located multiple
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