Deep Multiple Instance Learning for Airplane Detection in High Resolution Imagery

Automatic airplane detection in aerial imagery has a variety of applications. Two of the major challenges in this area are variations in scale and direction of the airplanes. In order to solve these challenges, we present a rotation-and-scale invariant airplane proposal generator. This proposal generator is developed based on the symmetric and regular boundaries of airplanes from the top view called symmetric line segments (SLS). Then, the generated proposals are used to train a deep convolutional neural network for removing non-airplane proposals. Since each airplane can have multiple SLS proposals, where some of them are not in the direction of the fuselage, we collect all proposals correspond to one ground truth as a positive bag and the others as the negative instances. To have multiple instance deep learning, we modify the training approach of the network to learn from each positive bag at least one instance as well as all negative instances. Finally, we employ non-maximum suppression to remove duplicate detections. Our experiments on NWPU VHR-10 dataset show that our method is a promising approach for automatic airplane detection in very high resolution images. Moreover, the proposed algorithm can estimate the direction of the airplanes using box-level annotations as an extra achievement.