Oil Tank Extraction Based on Joint-Spatial Saliency Analysis for Multiple SAR Images

Abstract

The lack of true color and the presence of background clutter reduce the accuracy rate of the saliency analysis for oil tank extraction in the synthetic aperture radar (SAR) images. This letter proposes a specially designed unsupervised method to extract oil tanks using the joint-spatial saliency analysis (JSSA) for multiple SAR images. First, the intrasaliency analysis is established on a saliency driven iterative clustering. This considers the spatial intensity and texture feature within a single image and suppresses most backgrounds. Second, the cospatial residual and the local grayscale statistics are considered independently in the intersaliency analysis. The common salient parts among the input series are extracted and used to overcome the problem of the lack of true color. Third, to make the fusion of the two kinds of saliency maps, the low-rank matrix is introduced. The weights of different maps are calculated and the saliency cues are integrated efficiently. Finally, after the statistics of the highlight points within the candidates, the location of the oil tanks is refined. The experiments show the superiority of the proposed method in both the pixel level and the geometric segmentation. The result of the JSSA model appears to improve the accuracy with fewer missing objects compared with the competing algorithms.