Cross-Time and Orientation-Invariant Overhead Image Geolocalization Using Deep Local Features

Yuxin Tian, Xueqing Deng, Yi Zhu, Shawn Newsam; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 2512-2520

Overhead image geolocalization is becoming increasingly important due to the growing collection of drone imagery without location information. In this paper, we perform large-scale overhead image geolocalization by matching a query image to wide-area reference imagery with known location. We use deep local features so that the query image need not align with but only overlap the tiled reference imagery. We further address two key challenges. For when the query and reference imagery are from different dates, we perform cross-time geolocalization using time invariant features learned using a Siamese network. For when the query and reference imagery are oriented differently, we introduce an orientation normalization network. We demonstrate our contributions on two new high-resolution overhead image datasets. Our method significantly outperforms strong baselines on cross-time geolocalization and is shown to exhibit promising orientation invariance.