Owing to the relatively small size of vehicles in remote sensing images, lacking sufficient detailed appearance to distinguish vehicles from similar objects, the detection performance is still far from satisfactory compared with the detection results on everyday images. Inspired by the positive effects of super-resolution convolutional neural network (SRCNN) for object detection and the stunning success of deep CNN techniques, we apply generative adversarial network frameworks to realize simultaneous SRCNN and vehicle detection in an end-to-end manner, and the detection loss is backpropagated into the SRCNN during training to facilitate detection. In particular, our work is unsupervised and bypasses the requirement of low-/high-resolution image pairs during the training stage, achieving increased generality and applicability. Extensive experiments on representative data sets demonstrate that our method outperforms the state-of-the-art detectors. (The source code will be made available after the review process.)

Vehicle detection in remote sensing images has attracted remarkable attention for its important role in a variety of applications in traffic, security, and military fields. Motivated by the stunning success of region convolutional neural network (R-CNN) techniques, which have achieved the state-of-the-art performance in object detection task on benchmark data sets, we propose to improve the Faster R-CNN method with better feature extraction, multiscale feature fusion, and homography data augmentation to realize vehicle detection in remote sensing images. Extensive experiments on representative remote sensing data sets related to vehicle detection demonstrate that our method achieves better performance than the state-of-the-art approaches. The source code will be made available (after the review process).