Airport runway detection in satellite images by Adaboost learning

Ugur Zongur; Ugur Halici; Orsan Aytekin; Ilkay Ulusoy

doi:10.1117/12.830295

28 September 2009 Airport runway detection in satellite images by Adaboost learning

Ugur Zongur, Ugur Halici, Orsan Aytekin, Ilkay Ulusoy

Proceedings Volume 7477, Image and Signal Processing for Remote Sensing XV; 747708 (2009) https://doi.org/10.1117/12.830295
Event: SPIE Remote Sensing, 2009, Berlin, Germany

Abstract

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.

Citation Download Citation

Ugur Zongur, Ugur Halici, Orsan Aytekin, and Ilkay Ulusoy "Airport runway detection in satellite images by Adaboost learning", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 747708 (28 September 2009); https://doi.org/10.1117/12.830295

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available