Detecting artificial targets, such as aircraft, in satellite images is significant in military and civil applications. Although the performance has improved with the use of more complicated features and better learning methods, effectively handling aircraft with variations of type, pose, and size is still very challenging. To solve this problem, we propose a multiscale sliding-window framework based on aggregate channel features, well-designed features that contain rich information. We also employ a fast feature pyramids algorithm to accelerate multiscale aircraft detection. In this framework, features are trained by Cascade AdaBoost including multiple rounds of bootstrapping that leads to improved overall accuracy. A two-step nonmaximum suppression algorithm is carefully designed based on a given set of detections. Our method shows a competitive performance on the QuickBird images of 0.6 m resolution.