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In this paper, we focus on the task of multi-view multi-sourcegeo-localization, which serves as an important auxiliary method of GPSpositioning by matching drone-view image and satellite-view image withpre-annotated GPS tag. To solve this problem, most existing methods adoptmetric loss with an weighted classification block to force the generation ofcommon feature space shared by different view points and view sources. However,these methods fail to pay sufficient attention to spatial information(especially viewpoint variances). To address this drawback, we propose anelegant orientation-based method to align the patterns and introduce a newbranch to extract aligned partial feature. Moreover, we provide a stylealignment strategy to reduce the variance in image style and enhance thefeature unification. To demonstrate the performance of the proposed approach,we conduct extensive experiments on the large-scale benchmark dataset. Theexperimental results confirm the superiority of the proposed approach comparedto state-of-the-art alternatives.

Multi-view Drone-based Geo-localization via Style and Spatial Alignment