Saliency-Driven Oil Tank Detection Based on Multidimensional Feature Vector Clustering for SAR Images

Abstract

A novel saliency-driven oil tank detection method based on multidimensional feature vector clustering (MFVC) is proposed in this letter for synthetic aperture radar (SAR) images. There are three major contributions: 1) a specially designed MFVC method, which is suitable for SAR images without true colors, is employed to detect oil tanks roughly by saliency analysis. Five important complementary features, including intensity, texture, structure, and 2-D coordinates, form a 5-D vector, and then an unsupervised strategy is employed for clustering the 5-D feature vectors to acquire the saliency map; 2) For accurate location, the shape limit coefficient is added to the original active contour model to extract contours of top surfaces; and 3) according to the relations of top, bottom, and the brightest arch, bottoms of oil tanks are computed precisely. Experiments are conducted in two aspects: evaluation for saliency analysis, and for bottom location. Results show that the MFVC method outperforms competing methods in maintaining complete oil tanks and accurate boundaries, and removing the background clutter as much as possible.