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Vehicle detection from high-resolution aerial image facilitates the study of the public traveling behavior on a large scale. In the contextof road, a simple and effective algorithm is proposed to extract the texture-salient vehicle among the pavement surface. Texturallyspeaking, the majority of pavement surface changes a little except for the neighborhood of vehicles and edges. Within a certaindistance away from the given vector of the road network, the aerial image is decomposed into a smoothly-varying cartoon part and anoscillatory details of textural part. The variational model of Total Variation regularization term and L1 fidelity term (TV-L1) is adoptedto obtain the salient texture of vehicles and the cartoon surface of pavement. To eliminate the noise of texture decomposition, regionsof pavement surface are refined by seed growing and morphological operation. Based on the shape saliency analysis of the centralobjects in those regions, vehicles are detected as the objects of rectangular shape saliency. The proposed algorithm is tested with adiverse set of aerial images that are acquired at various resolution and scenarios around China. Experimental results demonstrate thatthe proposed algorithm can detect vehicles at the rate of 71.5% and the false alarm rate of 21.5%, and that the speed is 39.13 secondsfor a 4656 x 3496 aerial image. It is promising for large-scale transportation management and planning

VEHICLE DETECTION OF AERIAL IMAGE USING TV-L1 TEXTURE DECOMPOSITION