Multi-view Drone-based Geo-localization via Style and Spatial Alignment
- URL: http://arxiv.org/abs/2006.13681v2
- Date: Thu, 9 Jul 2020 03:29:57 GMT
- Title: Multi-view Drone-based Geo-localization via Style and Spatial Alignment
- Authors: Siyi Hu and Xiaojun Chang
- Abstract summary: Multi-view multi-source geo-localization serves as an important auxiliary method of GPS positioning by matching drone-view image and satellite-view image with pre-annotated GPS tag.
We propose an elegant orientation-based method to align the patterns and introduce a new branch to extract aligned partial feature.
- Score: 47.95626612936813
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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