  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 https://github.com/Xcco1/SA\_DOM
