Relative Distance Guided Dynamic Partition Learning for Scale-Invariant UAV-View Geo-Localization

• URL: http://arxiv.org/abs/2412.11535v2
• Date: Mon, 23 Dec 2024 14:17:30 GMT
• Title: Relative Distance Guided Dynamic Partition Learning for Scale-Invariant UAV-View Geo-Localization
• Authors: Quan Chen, Tingyu Wang, Rongfeng Lu, Bolun Zheng, Zhedong Zheng, Chenggang Yan,
• Abstract summary: UAV-view Geo-Localization(UVGL) presents substantial challenges, particularly due to the disparity in visual appearance between drone-captured imagery and satellite perspectives.<n>We propose a partition learning framework based on relative distance, which alleviates the dependence on scale consistency while mining fine-grained features.<n>Our approach achieves superior geo-localization accuracy across various scale-inconsistent scenarios, and exhibits remarkable robustness against scale variations.
• Score: 37.30243235827088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: UAV-view Geo-Localization~(UVGL) presents substantial challenges, particularly due to the disparity in visual appearance between drone-captured imagery and satellite perspectives. Existing methods usually assume consistent scaling factor across different views. Therefore, they adopt predefined partition alignment and extract viewpoint-invariant representation by constructing a variety of part-level features. However, the scaling assumption is not always hold in the real-world scenarios that variations of UAV flight state leads to the scale mismatch of cross-views, resulting in serious performance degradation. To overcome this issue, we propose a partition learning framework based on relative distance, which alleviates the dependence on scale consistency while mining fine-grained features. Specifically, we propose a distance guided dynamic partition learning strategy~(DGDPL), consisting of a square partition strategy and a distance-guided adjustment strategy. The former is utilized to extract fine-grained features and global features in a simple manner. The latter calculates the relative distance ratio between drone- and satellite-view to adjust the partition size, thereby explicitly aligning the semantic information between partition pairs. Furthermore, we propose a saliency-guided refinement strategy to refine part-level features, so as to further improve the retrieval accuracy. Extensive experiments show that our approach achieves superior geo-localization accuracy across various scale-inconsistent scenarios, and exhibits remarkable robustness against scale variations. The code will be released.

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