Detecting runways in complex airport scenes

Abstract Detection of runways in aerial images is part of a project to automatically map complex cultural areas such as a major commercial airport complex. This task is much more difficult than appears at first. Runways are not merely homogeneous strips in the image due to several markingson the surface, changes in the surface material and presence of other objects such as taxiways and aircraft. We use some generic sources of knowledge to help with these problems in a hypothesize and test paradigm. Hypotheses are formed by looking for instances of long rectangular shapes, possibly interrupted by other long rectangles. Runway markings, mandated by standards for runway construction, are used to verify our hypotheses. Our system gives good performance on a variety of complex scenes and does not rely on location specific knowledge.

[1]  Gérard G. Medioni,et al.  Fast Convolution with Laplacian-of-Gaussian Masks , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  K. Ramesh Babu,et al.  Linear Feature Extraction and Description , 1979, IJCAI.

[3]  David M. McKeown,et al.  Automating Knowledge Acquisition For Aerial Image Interpretation , 1987, Photonics West - Lasers and Applications in Science and Engineering.

[4]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  David G. Lowe,et al.  Perceptual Organization and Visual Recognition , 2012 .

[6]  Ramakant Nevatia,et al.  Detecting buildings in aerial images , 1988, Comput. Vis. Graph. Image Process..

[7]  S. Palmer.  The Psychology of Perceptual Organization: A Transformational Approach , 1983 .

[8]  Lambert E. Wixson,et al.  Automating knowledge acquisition for aerial image interpretation , 1989, Comput. Vis. Graph. Image Process..

[9]  Gérard G. Medioni,et al.  Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  John P. McDermott,et al.  Rule-Based Interpretation of Aerial Imagery , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.