Airport recognition remains an important and challenging topic for research. In general, there are four main steps in the process of airport recognition: pre-processing of remote sensing images, features extraction, rough location and the recognition of airport. This paper puts forward an automatic airport recognition method which adopts improved methods in each step. In pre-processing, an edge-preserve image smoothing algorithm based on Convexity Model is developed. In features extraction, the Canny operator and chain codes are used. An improved Κ-Means lines segmentation and estimate rules are used to find candidate areas in rough location of the airport. And those candidate regions are binarized and prior knowledge is used in airport recognition. Experimental data and application results show that the above methods are efficient and enhance the accuracy in airport recognition.