Airport Extraction via Complementary Saliency Analysis and Saliency-Oriented Active Contour Model

Abstract

Automatic airport extraction in remote sensing images (RSIs) has been widely applied in military and civil applications. An efficient airport extraction framework for RSIs is constructed in this letter. In the first step, we put forward a two-way complementary saliency analysis (CSA) scheme that combines vision-oriented saliency and knowledge-oriented saliency for the airport position estimation. In the second step, we construct a saliency-oriented active contour model (SOACM) for airport contour tracking, where a saliency orientation term is incorporated into the level-set-based energy functions. Under the guidance of saliency feature representations obtained by CSA, the SOACM can acquire well-defined and highly precise object contours. Experimental results demonstrate that the proposed extraction framework shows good adaptability in remote sensing scenes, and uniformly achieves high detection rate and low false alarm rate. Compared with three state-of-the-art algorithms, our proposal can not only estimate the location of airport targets, but also extract detailed information of the airport contours.