Airport detection in large aerial optical imagery

A method to detect airports in large aerial optical imagery is considered. Combining texture segmentation and shape detection, this method shows advantages in analyzing large aerial imagery. First, large aerial images are segmented and interpreted according to textural features using a fast kernel matching pursuits (KMP) algorithm. As a result, attention is then paid on small regions of interest, extracted from the large images. Second, for each region of interest, a corresponding binary image is generated via the Canny edge operator, yielding a modified Hough transform image with which we search for elongated rectangles with desired dimensions (characteristic of runways). Those detected rectangles are declared as runways and the corresponding region of interest as an airport. Application in a dozen aerial images from southern California demonstrates the effectiveness of the algorithm.