In this letter, a novel oil tank detection framework based on a shape-guide saliency (SGS) model is proposed. Beyond the low-level visual stimuli, SGS focuses more on simulating the selective visual searching, which is dominated by the goal in human minds. Using a top–down strategy, SGS breaks the limitation of the low-level visual features and introduces the high-level task concept to measure saliency. For the oil tank detection, SGS model skillfully extracts the contour shape cue (CSC) as the target-oriented information and uses CSC to guide the selective saliency value calculation. Specifically, a sparse reconstruction with the target-specific dictionary is implemented to generate the saliency map. This saliency map only assigns high values to oil tank regions instead of highlighting all high-contrast regions. Consequently, SGS model is capable of accurately locating oil tanks and eliminating the interferences of high-contrast backgrounds. Experimental results on a remote sensing data set demonstrate that the proposed SGS model outperforms five class-independent saliency models. Comparisons with the state-of-the-art oil tank detection approaches demonstrate the effectiveness of the proposed method.