Abstract
Aircraft detection is one of the applications of high resolution remote-sensing images. Conventional method of aircraft detection use deep and complicated network, which needs highly computing cost. In this letter, an efficient and effective aircraft detection framework based on BOVW features and cascade AdaBoost classifier has been proposed. A variety of affine invariant features, which represent the complicate structure of aircraft, are extracted from sliding windows, and the direction estimation of aircraft is introduced to align the aircraft before detection. Besides, an accurate NMS algorithm is designed to make the target location more accurate. The proposed method is evaluated with recent advanced detection methods. The superior experimental outcome indicates that our framework achieves better accuracy as well as efficiency.
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