Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 7; p. 1578
• Main Authors: Pan, Zongxu, Liu, Lei, Qiu, Xiaolan, Lei, Bin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05-07-2017; MDPI
• Subjects: Algorithms; Censorship; False alarms; false alarms elimination; Gaofen-3 SAR images; Image detection; iterative CFAR approach; mean-shift based coarse detection; Normal distribution; Pixels; Radar imaging; Sensors; Synthetic aperture radar; ultrafine strip-map mode; Ultrafines; vessel detection; ultrafine strip-map mode; vessel detection; Gaofen-3 SAR images; mean-shift based coarse detection; iterative CFAR approach; false alarms elimination
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17071578

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 l 1 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.