Fast airplane detection with hierarchical structure in large scene remote sensing images at high spatial resolution

In order to detect airplane efficiently in large scene remote sensing images with high spatial resolution, a hierarchical detection framework is proposed according to the process of locating the target area in the downsampled image and then detecting the target in the original high-resolution image of that target region. First, we locate the airport in the downsampled original high-resolution image based on line features (midpoint coordinate and angle) clustering. A visual saliency model based on Itti model is then applied to detect airplanes candidates in the resulting airport region with the original high-resolution image. Finally, scale invariant feature transformation and support vector machine are used to classify the airplanes candidates. Experimental results indicate that the proposed method presents higher recall rate and detection speed. Meanwhile, the time cost of airport detection is much less than representative methods.