Airport detection in large aerial optical imagery

Dehong Liu, Lihan He, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

59 Scopus citations


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.
Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - Sep 27 2004
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2021-02-09


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