Abstract
Aiming at the aircraft target in visible light remote sensing image, this paper proposes a false alarm removal method for target detection under small sample training conditions. First, use data enhancement methods for small sample data to increase the number of samples and improve the generalization ability of features. Then, according to the clear outline of the aircraft target and the obvious characteristics of the edge features, the Haar feature plus AdaBoost algorithm is used to preliminary detect the aircraft target. Finally, the convolutional neural network is used to extract the target features, and the preliminary detection results are classified to remove the false alarm targets and improve the accuracy of the detection. The experimental results show that the detection effect is good in aircraft target detection.
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