Ground-to-Satellite Image-Based Geo-Localization

Abstract:

Image-based geo-localization is widely used in many applications due to its high accuracy and low cost. Recently ground-to-satellite image-based geo-localization is increasingly attracting the attention since the satellite imagery has covered the whole earth. Therefore the localization is possible to be applied in a much larger area. The problem of localization on a geo-referenced satellite map given a query ground view image remains challenging due to the drastic change in viewpoint that causes traditional image descriptors based matching to fail. We leverage on the recent success of deep learning to propose the CVM-Net for the cross-view image-based geo-localization task. Specifically, our network is based on a Siamese architecture to do metric learning for the matching task. We first use the fully convolutional layers to extract local image features, which are then encoded into global image descriptors using the powerful NetVLAD. As part of the training procedure, we also introduce a simple yet effective weighted soft margin triplet loss function that not only speeds up the training convergence but also improves the final matching accuracy. Experimental results show that our proposed network significantly outperforms the state-of-the-art approaches on two existing benchmarking datasets. Based on the CVM-Net, we propose a Markov localization framework that enforces the temporal consistency between image frames to enhance the geo-localization results in the case where a video stream of ground view images are available. Experimental results show that our proposed Markov localization framework can continuously localize the vehicle within a small error on our Singapore dataset.