论文标题
通过样式和空间对齐方式基于多视图无人机的地理位置定位
Multi-view Drone-based Geo-localization via Style and Spatial Alignment
论文作者
论文摘要
在本文中，我们专注于多视图多源地理位置的任务，该任务是通过与预通道的GPS标签匹配无人机视图图像和卫星视图图像来充当GPS定位的重要辅助方法。为了解决这个问题，大多数现有方法都采用度量损失，并通过加权分类块来迫使不同的观点和视图源共享的共同特征空间的产生。但是，这些方法无法充分关注空间信息（尤其是观点差异）。为了解决这一缺点，我们提出了一种基于优雅的基于方向的方法来对齐模式并引入一个新的分支来提取排列的部分特征。此外，我们提供了一种样式的对齐策略，以减少图像样式的差异并增强特征统一。为了证明所提出的方法的性能，我们对大规模基准数据集进行了广泛的实验。与最先进的替代方案相比，实验结果证实了所提出的方法的优势。
In this paper, we focus on the task of multi-view multi-source geo-localization, which serves as an important auxiliary method of GPS positioning by matching drone-view image and satellite-view image with pre-annotated GPS tag. To solve this problem, most existing methods adopt metric loss with an weighted classification block to force the generation of common 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 an elegant orientation-based method to align the patterns and introduce a new branch to extract aligned partial feature. Moreover, we provide a style alignment strategy to reduce the variance in image style and enhance the feature unification. To demonstrate the performance of the proposed approach, we conduct extensive experiments on the large-scale benchmark dataset. The experimental results confirm the superiority of the proposed approach compared to state-of-the-art alternatives.
