CAMP: A Cross-View Geo-Localization Method Using Contrastive Attributes Mining and Position-Aware Partitioning

Cross-view geo-localization (CVGL) task aims to utilize geographic data, such as maps or high-resolution satellite images, as reference to estimate the positions of a ground- or near-ground- captured query image. This task is particularly challenging due to the significant changes in visual appearance resulting from the extreme viewpoint variations. To address this challenge, a range of innovative methods have been proposed. However, intra-scene geometric information and inter-scene discriminative representation are not fully explored. In this article, we propose a novel CVGL method using contrastive attributes mining and position-aware partitioning (CAMP), which incorporates a position-aware partition branch (PPB) and a contrastive attributes mining (CAM) strategy. PPB learns fine-grained local features of different parts and captures their spatial information, providing a comprehensive understanding of scenes from both textual and spatial perspectives. CAM establishes supervision of the negative samples based on the images from the same platform, empowering the model to better discern differences between distinct scenes without extra memory cost. The proposed CAMP surpasses existing methods, achieving state-of-the-art results on the satellite-drone CVGL datasets University-1652 and SUES-200. Additionally, our method also outperforms existing methods in cross-dataset generalization, achieving an 8.85% increase in R@1 when trained on the University-1652 dataset and tested on the SUES-200 dataset at a height of 150 m. Our code and model are available at https://github.com/Mabel0403/CAMP.