SDPL: Shifting-Dense Partition Learning for UAV-View Geo-Localization
Quan Chen Tingyu Wang Zihao Yang Haoran Li Rongfeng Lu Yaoqi Sun Bolun Zheng Chenggang Yan
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 both appearance of targets and environmental content from
different views. Existing methods mainly focus on digging more comprehensive
information through feature maps segmentation, while inevitably destroy the
image structure and are sensitive to the shifting and scale of the target in
the query. To address the above issues, we introduce a simple yet effective
part-based representation learning, called shifting-dense partition learning
(SDPL). Specifically, we propose the dense partition strategy (DPS), which
divides the image into multiple parts to explore contextual-information while
explicitly maintain 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 select the best partitions. Extensive
experiments show that our SDPL is robust to position shifting and scale
variations, and achieves competitive performance on two prevailing benchmarks,
i.e., University-1652 and SUES-200.