Abstract
This paper proposes an airport detection and recognition method for remote sensing image based on visual attention mechanism. Considering the disadvantage in traditional methods by which the remote sensing images are analyzed pixel by pixel, we introduce visual attention models into airport detection and improve the efficiency of automatic target detection greatly. Firstly, Hough transform is used to judge the existence of an airport and then the improved graph-based visual saliency (GBVS) visual attention model is used to extract regions of candidates (ROCs). According to the scale-invariant feature transform (SIFT) feature extracted from ROCs and classified by HDR tree, the airport areas are recognized. Experimental results show that the proposed method has faster speed, higher recognition rate and lower false alarm rate than other current methods, and is robust against white noise.
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