Abstract
This study aims to detect vessels with lengths ranging from about 70 to 300 m, in Gaofen-3 (GF-3) SAR images with ultrafine strip-map (UFS) mode as fast as possible. Based on the analysis of the characteristics of vessels in GF-3 SAR imagery, an effective vessel detection method is proposed in this paper. Firstly, the iterative constant false alarm rate (CFAR) method is employed to detect the potential ship pixels. Secondly, the mean-shift operation is applied on each potential ship pixel to identify the candidate target region. During the mean-shift process, we maintain a selection matrix recording which pixels can be taken, and these pixels are called as the valid points of the candidate target. The norm regression is used to extract the principal axis and detect the valid points. Finally, two kinds of false alarms, the bright line and the azimuth ambiguity, are removed by comparing the valid area of the candidate target with a pre-defined value and computing the displacement between the true target and the corresponding replicas respectively. Experimental results on three GF-3 SAR images with UFS mode demonstrate the effectiveness and efficiency of the proposed method.
Highlights
As an effective Earth observation means, synthetic aperture radar (SAR) technology has developed rapidly in recent years
The intensities of the four sub-images and the detected results of the proposed method are shown in Figure 7, and the red box in the image indicates the detected vessel
We propose a fast vessel detection method in GF-3 SAR images with ultrafine strip-map (UFS)
Summary
As an effective Earth observation means, synthetic aperture radar (SAR) technology has developed rapidly in recent years. SAR vessel detection is an important application in the field of marine surveillance and has received much attention recently. A SAR ship detection system usually consists of four stages: land masking, preprocessing, prescreening, and discrimination [1]. The land masking stage aims to distinguish the land from the sea surface. The preprocessing step aims to make the subsequent detection easier, and speckle filtering is a widely used preprocessing step. Certain algorithms are applied to search the entire image for potential ship pixels. Experience has shown that no algorithm can perfectly detect all ship pixels without introducing false alarms, a follow up discrimination stage is usually needed to remove these false alarms
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