Most existing airport detection methods for remote sensing image utilizes linear features of the airport runway insufficiently, and the computational complexity is high due to multi-scale anchor matching and global searching within full image. To solve this problem, an airport detection method based on saliency fusion of parallel lines and regions of interest is presented in this paper. Firstly the parallelism feature of the airport runway is extracted based on the prior knowledge of airport, and then regions of interest (ROI) is obtained according to the improved graph based visual saliency (GBVS). The airport is then located through the saliency fusion of parallel lines and regions of interest. Finally airport detection is achieved by transfer learning. The experimental results demonstrate that the proposed method is more advantageous than the comparison algorithms in terms of detection accuracy, processing speed, and false alarm rate. Moreover, the method only requires a small amount of samples for model training.