Scale-adaptive UAV Geo-localization via Height-aware Partition Learning

TL;DR

This paper introduces a scale-adaptive UAV geo-localization method that uses drone height information to dynamically adjust feature partitions, improving accuracy in scale-varying scenarios.

Contribution

It proposes a height-aware adjustment strategy integrated into SaLPN to handle scale mismatches caused by flight height variations, advancing cross-view geo-localization.

Findings

Achieves state-of-the-art accuracy in scale-inconsistent scenarios.

Demonstrates robustness against scale variations.

Outperforms existing methods in UAV geo-localization tasks.

Abstract

UAV Geo-Localization faces significant challenges due to the drastic appearance discrepancy between dronecaptured images and satellite views. Existing methods typically assume a consistent scaling factor across views and rely on predefined partition alignment to extract viewpoint-invariant representations through part-level feature construction. However, this scaling assumption often fails in real-world scenarios, where variations in drone flight states lead to scale mismatches between cross-view images, resulting in severe performance degradation. To address this issue, we propose a scale-adaptive partition learning framework that leverages known drone flight height to predict scale factors and dynamically adjust feature extraction. Our key contribution is a height-aware adjustment strategy, which calculates the relative height ratio between drone and satellite views, dynamically adjusting partition sizes to explicitly align semantic information between partition pairs. This strategy is integrated into a Scale-adaptive Local Partition Network (SaLPN), building upon an existing square partition strategy to extract both finegrained and global features. Additionally, we propose a saliencyguided refinement strategy to enhance part-level features, further improving retrieval accuracy. Extensive experiments validate that our height-aware, scale-adaptive approach achieves stateof-the-art geo-localization accuracy in various scale-inconsistent scenarios and exhibits strong robustness against scale variations. The code will be made publicly available.