Aircraft detection in remote sensing images based on a deep residual network and Super-Vector coding

Aircraft detection in remote sensing images has become an attractive research topic, which plays an essential role in various military and civil applications. In this letter, we develop a novel method for aircraft detection in remote sensing images based on deep residual network (ResNet) and Super-Vector (SV) coding. First, a variant of ResNet with fewer layers is designed to increase the resolution of the feature map, and multi-level convolutional features are merged into an informative feature description for region proposal. Meanwhile, we extract histogram of oriented gradient (HOG) with SV coding from each region of interest, which assists convolutional features to complete object classification. We comprehensively evaluate the proposed method on our remote sensing image dataset. The experimental results show that our method outperforms top-performing aircraft detection methods with higher accuracy even when the backgrounds are complicated.