Airport Target Detection in Remote Sensing Images: A New Method Based on Two-Way Saliency

Abstract

The geometrical features of airport line segments are seldom used by traditional methods for airport detection in panchromatic remote sensing images. This letter presents a novel method based on both bottom-up (BU) saliency and top-down saliency. Noticing that airport runways have features of vicinity and parallelity and that their lengths are among a certain range, we introduce the concept of near parallelity for the first time and treat it as prior knowledge that can fully exploit the geometrical relationship of airport runways. Meanwhile, a simplified graph-based visual saliency model is used to extract the BU saliency. Two-way results are combined, and candidate regions can be derived from it. Finally, a scale-invariant feature transform and a support vector machine are used to determine whether the regions contain airports or not. The proposed method is tested on an image data set composed of different kinds of airports. The experimental results show that the method outperforms other state-of-the-art models in terms of speed, the detection rate, and the false-alarm rate. In addition, the method is more robust to a complex background than the other methods.