Abstract:

A method to detect airports in large space-borne synthetic aperture radar (SAR) imagery is studied. First, the large SAR imagery is segmented according to amplitude characteristics using maximum a posteriori (MAP) estimator based on the heavytailed Rayleigh model. The attention is then paid on the object of interest (OOI) extracted from the large images. The minimumarea enclosing rectangle (MER) of OOI is created via a rotating calipers algorithm. The projection histogram (PH) of MER for OOI is then computed and the scale and rotation invariant feature for OOI are extracted from the statistical characteristics of PH. A support vector machine (SVM) classifier is trained using those feature parameters and the airport is detected by the SVM classifier and Hough transform. The application in space-borne SAR images demonstrates the effectiveness of the proposed method.

Key words: synthetic aperture radar (SAR) imagery, airport detection, image segmentation, minimum-area enclosing rectangle, support vector machine (SVM)