GeoCapsNet: Aerial to Ground view Image Geo-localization using Capsule Network
This addresses the problem of accurate geo-localization for applications like navigation and mapping, though it is incremental as it builds on existing capsule network and triplet loss techniques.
The paper tackles cross-view image geo-localization by matching ground-view images to GPS-tagged aerial images using a capsule network, achieving significant improvements over state-of-the-art methods on two benchmark datasets.
The task of cross-view image geo-localization aims to determine the geo-location (GPS coordinates) of a query ground-view image by matching it with the GPS-tagged aerial (satellite) images in a reference dataset. Due to the dramatic changes of viewpoint, matching the cross-view images is challenging. In this paper, we propose the GeoCapsNet based on the capsule network for ground-to-aerial image geo-localization. The network first extracts features from both ground-view and aerial images via standard convolution layers and the capsule layers further encode the features to model the spatial feature hierarchies and enhance the representation power. Moreover, we introduce a simple and effective weighted soft-margin triplet loss with online batch hard sample mining, which can greatly improve image retrieval accuracy. Experimental results show that our GeoCapsNet significantly outperforms the state-of-the-art approaches on two benchmark datasets.