Airport Detection Based on Saliency Analysis and Geometric Feature Detection for Remote Sensing Images

Abstract

Owing to the complicated background information and large data volume in remote sensing (RS) images, it's difficult to detect airport precisely and efficiently. In this paper, we propose a credible airport detection method based on saliency analysis and geometric feature detection. On the one hand, we use a novel saliency analysis model to measure both global contrast and spatial unity in RS images, by which the most salient region can be extracted accurately and the background can be suppressed preferably. On the other hand, considering the geometric features of the airport, a feature descriptor is conducted to detect proper hole structures and line segments in the saliency map. The experimental results indicate that our proposal outperforms existing saliency analysis models and shows good performance in the detection of the airport.