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 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 the image retrieval accuracy. Experimental results show that our GeoCapsNet significantly outperforms the state-of-the-art approaches on two benchmark datasets.
GEOCAPSNET: Ground to Aerial View Image Geo-Localization using Capsule Network
Bin Sun,Chen Chen,Yingying Zhu,Jianmin Jiang
Published 2019 in IEEE International Conference on Multimedia and Expo
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- Publication year
2019
- Venue
IEEE International Conference on Multimedia and Expo
- Publication date
2019-04-12
- Fields of study
Computer Science, Engineering, Environmental Science
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