Local context deformable parts model-based method for remote sensing airport aircraft detection

As one of the most challenging tasks of optical remote sensing object detection, airport aircraft detection has attracted more attention from researchers. However, various scales and orientation of aircraft, and the complexity of airport regions leads to low detection performance. To address these problems, this article presents an effective aircraft detection method called the local context (LC) deformable parts model. First, aircraft candidate region fast extraction is developed to improve the detection efficiency. Then, the LC weight map is constructed by weighting the key points with the aircraft local characteristics. Afterward, gradient features are combined with the weight value to establish the LC histogram of oriented gradient feature pyramid. Finally, based on the established feature pyramid, aircraft candidate region is identified efficiently by orientation prediction of the suspected target. A series of comparison experiments on two datasets demonstrate that the proposed method has higher detection accuracy and efficiency than the existing detection methods.