Optik uydu görüntülerinde havaalanı pistlerinin tespit edilmesi.

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. Automatic detection of airports is particularly essential, due to the strategic importance of these targets. In this thesis, a detection method is proposed for airport runways, which is the most distinguishing element of an airport. This method, which operates on large optical satellite images, is composed of a segmentation process based on textural properties, and a runway shape detection stage. In the segmentation process, 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, 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, as a result of the segmentation operation. Subsequently, the runway shape detection stage, based on a novel form of Hough Transform, is performed over the possible runway locations, in order to obtain final runway positions. The proposed algorithm is examined with experimental work using a comprehensive data set consisting of large and high resolution satellite images and successful results are achieved.M.S. - Master of Scienc