A Large-Kernel Perceptual Attention Network for Robust Multiview Geo-Localization With Drone-View Offset Adaptation

Abstract

Multiview geo-localization utilizing drone and satellite imagery offers a reliable alternative to GPS-based positioning in challenging environments such as urban canyons and electromagnetically degraded areas. A key challenge in this task stems from the spatial misalignment between drone-view and satellite-view images, particularly when target buildings appear off-center due to varying flight attitudes and environmental disturbances. Existing methods, which often rely on implicit center-aligned assumptions, show limited robustness under such offset conditions. To address this limitation, we construct a novel drone-view offset dataset named Offset-1652 by applying controlled translational transformations to the benchmark dataset, simulating realistic displacement scenarios along horizontal, vertical, and diagonal directions. Furthermore, we propose a large-kernel perceptual attention network (LK-PAN) that employs large-kernel depthwise convolutions to expand the receptive fields, thereby capturing global contextual information even for off-center targets. A symmetric InfoNCE loss is introduced to enhance cross-modal feature alignment and improve discrimination of hard negative samples. Comprehensive experiments conducted on both established benchmark datasets and the newly developed Offset-1652 and Offset-1652-MIX datasets demonstrate that the proposed method significantly outperforms existing approaches under a wide range of offset conditions. These results confirm its superior robustness and generalization capability for practical multiview geo-localization in complex operational environments. The source code and datasets are available at https://github.com/HAORANJY/LK-PAN-main