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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 (001) extracted from the large images. The minimumarea enclosing rectangle (MER) of 001 is created via a rotating calipers algorithm. The projection histogram (PH) of MER for 001 is then computed and the scale and rotation invariant feature for 001 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.