发表机构
School of Electronics and Information Engineering, Harbin Institute of Technology; National Key Laboratory of Radar Detection and Sensing, Nanjing Research Institute of Electronics Technology; School of Electrical and Electronic Engineering, University of Manchester（哈尔滨工业大学电子与信息工程学院; 南京电子技术研究所雷达探测与感知国家重点实验室; 曼彻斯特大学电气与电子工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机-卫星跨视角地理定位在实际飞行中受多种因素影响、现有方法稳健性不足的问题，提出ReLATE框架，通过可靠性自适应特征融合提升稳健性，在UAVSat-Deg基准测试中表现最佳，且干净图像精度具竞争力。
AI 中文摘要
无人机-卫星跨视角地理定位将无人机图像与卫星图像匹配，在干净图像基准上已取得令人印象深刻的精度。但在实际飞行中，无人机观测常受不利天气、光照变化等影响，现有方法在此类退化下的稳健性未被充分检验。本文提出UAVSat-Deg，一个用于退化无人机-卫星地理定位的大规模稳健性基准。通过该基准测试发现现有方法存在稳健性差距。为此提出ReLATE框架，在描述符构建过程中实现可靠性自适应特征融合，估计视觉令牌的结构平滑可靠性场，聚合可信局部证据并自适应集成到查询派生表示中，最终在干净图像上保持竞争精度，在退化测试中表现最佳。代码和数据集将在指定网址提供。
英文摘要
Unmanned aerial vehicle (UAV)-satellite cross-view geo-localization matches UAV images against satellite imagery and has achieved impressive accuracy on clean (non-degraded) image benchmarks. In real-world flights, however, UAV observations are frequently affected by adverse weather, illumination changes, platform motion, sensor noise, and compression, while the robustness of existing methods under such degradations remains largely unexamined. In this paper, we present UAVSat-Deg, a large-scale robustness benchmark for degraded UAV-satellite geo-localization, comprising University-1652-Deg and SUES-200-Deg. UAVSat-Deg covers 27 corruption types, including 19 core and 8 compound corruptions, at three severity levels, supports bidirectional drone-to-satellite and satellite-to-drone retrieval as well as multi-height UAV acquisition, and contains more than 11.7 million pre-generated corrupted test images. Benchmarking representative methods under this protocol reveals substantial robustness gaps, particularly under severe and compound corruptions. To address this problem, we propose ReLATE, a Reliable Evidence Learning framework with Adaptive Token Evidence Regulation, which realizes reliability-adaptive feature fusion during descriptor construction. ReLATE estimates a structure-smoothed reliability field over visual tokens, aggregates trustworthy local evidence, and adaptively integrates it into query-derived representations; the regulated query representations are then combined with the CLS-token and GeM-pooled branches to form the final cross-view descriptor. Across both test sets and retrieval directions, ReLATE achieves the best average corrupted-test performance among the compared methods while maintaining competitive accuracy on clean images. The code and dataset will be available at https://github.com/JHC626/ReLATE.
