Airport Detection Based on Salient Areas in Remote Sensing Images

In this paper we propose a new airport detection and recognition method for remote sensing images based on salient areas with the introduction of visual attention models to improve the efficiency of automatic target detection.Firstly,Hough transform is used to judge the existence of an airport,and then the improved graph-based visual saliency(GBVS) model is used to extract regions of candidates(ROCs).According to the features with scale-invariant feature transform(SIFT),which are extracted from ROCs and classified by hierarchical discriminant regression(HDR) tree,the airport areas are recognized.Experimental results show that our method is faster with higher recognition rate and lower false alarm rate than other current methods,and is robust against white noise.