The task of UAV-view geo-localization is to estimate the localization of a
query satellite/drone image by matching it against a reference dataset
consisting of drone/satellite images. Though tremendous strides have been made
in feature alignment between satellite and drone views, vast differences in
both inter and intra-class due to changes in viewpoint, altitude, and lighting
remain a huge challenge. In this paper, a style alignment based dynamic
observation method for UAV-view geo-localization is proposed to meet the above
challenges from two perspectives: visual style transformation and surrounding
noise control. Specifically, we introduce a style alignment strategy to
transfrom the diverse visual style of drone-view images into a unified
satellite images visual style. Then a dynamic observation module is designed to
evaluate the spatial distribution of images by mimicking human observation
habits. It is featured by the hierarchical attention block (HAB) with a
dual-square-ring stream structure, to reduce surrounding noise and geographical
deformation. In addition, we propose a deconstruction loss to push away
features of different geo-tags and squeeze knowledge from unmatched images by
correlation calculation. The experimental results demonstrate the
state-of-the-art performance of our model on benchmarked datasets. In
particular, when compared to the prior art on University-1652, our results
surpass the best of them (FSRA), while only requiring 2x fewer parameters. Code
will be released at this https URL

提出了一种基于风格对齐的动态观测方法，用于无人机视野地理定位，该方法从视觉风格转换和周围噪声控制两个方面应对挑战，并且通过降低周围噪声和地理变形的双方形环流结构的分层注意力块 (HAB) 提高了模型的性能，实验结果在基准数据集上显示出其良好的性能。