A Method for Detecting Aircraft Small Targets in Remote Sensing Images by Using CNNs Fused With Handcrafted Features

Aircraft target detection is a challenging task in remote sensing images, especially for aircraft small target detection. The most advanced object detection framework currently processes all information in the image uniformly through a deep neural network. In the past, in the process of detecting aircraft small targets, the feature extraction process was carefully designed, and handcrafted features were derived from expert knowledge or historical data, which included prior knowledge that was conducive to object detection. Embedding prior features into deep neural networks can enhance the saliency of target information and improve the detection performance of the model. Accordingly, this letter proposes a handcrafted feature fusion stream (HFFS) for embedding prior knowledge. We obtain handcrafted features based on the grayscale co-occurrence matrix and edge extraction operator and generate an attention map in deep convolutional neural networks (CNNs) to achieve the fusion of handcrafted feature maps and high-level feature maps in deep convolutional networks. The experimental results show that using HFFS on the baseline model improves the detection performance of the model for aircraft small targets. Compared with the baseline model, our detection model achieves improvements of 1.1% AR, 1.6% AP@0.5, and 1.6% AP@0.5:0.95 in the proposed dataset.