  Cross-view geo-localization is a task of matching the same geographic image
from different views, e.g., unmanned aerial vehicle (UAV) and satellite. The
most difficult challenges are the position shift and the uncertainty of
distance and scale. Existing methods are mainly aimed at digging for more
comprehensive fine-grained information. However, it underestimates the
importance of extracting robust feature representation and the impact of
feature alignment. The CNN-based methods have achieved great success in
cross-view geo-localization. However it still has some limitations, e.g., it
can only extract part of the information in the neighborhood and some scale
reduction operations will make some fine-grained information lost. In
particular, we introduce a simple and efficient transformer-based structure
called Feature Segmentation and Region Alignment (FSRA) to enhance the model's
ability to understand contextual information as well as to understand the
distribution of instances. Without using additional supervisory information,
FSRA divides regions based on the heat distribution of the transformer's
feature map, and then aligns multiple specific regions in different views one
on one. Finally, FSRA integrates each region into a set of feature
representations. The difference is that FSRA does not divide regions manually,
but automatically based on the heat distribution of the feature map. So that
specific instances can still be divided and aligned when there are significant
shifts and scale changes in the image. In addition, a multiple sampling
strategy is proposed to overcome the disparity in the number of satellite
images and that of images from other sources. Experiments show that the
proposed method has superior performance and achieves the state-of-the-art in
both tasks of drone view target localization and drone navigation. Code will be
released at https://github.com/Dmmm1997/FSRA
