Airport localization based on contextual knowledge complementarity in large scale remote sensing images

Localization of airport object region is an important task of object detection, which also is a significant application in remote sensing image processing. Because a single feature cannot fully describe the object, some non-airport images with flat line characteristics (such as road) or similar texture characteristics with the airport can be detected as the existing airport regions, which brings difficulties to subsequent object recognition and change detection. To address the above problems, a new airport localization method based on contextual knowledge complementarity in large scale remote sensing images is proposed. This new method first makes the utmost of the contextual information of the airport region in remote sensing image to construct a feature dictionary base. This dictionary base contains shallow vision knowledge and high semantic knowledge. Then the saliency maps of extracted knowledge are obtained and fused. A simple Ostu segmentation method is adopted to remove the false alarms and obtain the final airport region. It also gives the relative position coordinate and acreage of the detected airport. Compared with two state-of-the-art airport extraction methods through abundant experiments, the results show that the proposed airport localization method can fast and accurate locate the airport region, which has better effectiveness and robustness.