  Cross-view geo-localization aims to match images of the same target from
different platforms, e.g., drone and satellite. It is a challenging task due to
the changing appearance of targets and environmental content from different
views. Most methods focus on obtaining more comprehensive information through
feature map segmentation, while inevitably destroying the image structure, and
are sensitive to the shifting and scale of the target in the query. To address
the above issues, we introduce simple yet effective part-based representation
learning, shifting-dense partition learning (SDPL). We propose a dense
partition strategy (DPS), dividing the image into multiple parts to explore
contextual information while explicitly maintaining the global structure. To
handle scenarios with non-centered targets, we further propose the
shifting-fusion strategy, which generates multiple sets of parts in parallel
based on various segmentation centers, and then adaptively fuses all features
to integrate their anti-offset ability. Extensive experiments show that SDPL is
robust to position shifting, and performs com-petitively on two prevailing
benchmarks, University-1652 and SUES-200. In addition, SDPL shows satisfactory
compatibility with a variety of backbone networks (e.g., ResNet and Swin).
https://github.com/C-water/SDPL release.
