Airport runway detection in satellite images by Adaboost learning

Advances in hardware and pattern recognition techniques, along with the widespread utilization of remote sensing satellites, have urged the development of automatic target detection systems in satellite images. Automatic detection of airports is particularly essential, due to the strategic importance of these targets. In this paper, a runway detection method using a segmentation process based on textural properties is proposed for the detection of airport runways, which is the most distinguishing element of an airport. Several local textural features are extracted including not only low level features such as mean, standard deviation of image intensity and gradient, but also Zernike Moments, Circular-Mellin Features, Haralick Features, as well as features involving Gabor Filters, Wavelets and Fourier Power Spectrum Analysis. Since the subset of the mentioned features, which have a role in the discrimination of airport runways from other structures and landforms, cannot be predicted trivially, Adaboost learning algorithm is employed for both classification and determining the feature subset, due to its feature selector nature. By means of the features chosen in this way, a coarse representation of possible runway locations is obtained. Promising experimental results are achieved and given.