A Novel Saliency-Driven Oil Tank Detection Method For Synthetic Aperture Radar Images

Synthetic aperture radar (SAR) imaging system plays an important role in earth observation research. This leads to the significance of target detection in SAR image. In this paper, we propose a novel saliency-driven oil tank detection method (SDD) for SAR images. First, we use the enhanced directional smoothing (EDS) to remove speckle noise from SAR images; in the step of saliency analysis, the integer wavelet transforms (IWT) and the DoG filter are used to obtain orientation and intensity features, respectively. Then, the orientation feature map and the intensity feature map resulting from these two kinds of features are utilized to compute the final saliency map; after segmenting the saliency map, the obtained connected domain guide the Active Contour Model (ACM) to acquire accurate contours of tops of oil tanks, and the bottoms of the oil tanks can be detected by the strong scattering points around the tops. Experimental results show that the proposed model outperforms the classical/state-of-the-art models in maintaining complete targets and accurate boundaries.