Cross-view geo-localization, which aims to match images of the same scene captured from diverse viewpoints from drone and satellite, presents a persistent challenge due to significant geometric distortions and appearance variations. Existing methods lack a comprehensive exploration and dynamic integration of spatial and channel attention mechanisms, while primarily focusing on extracting single-scale features. The former results in the model failing to focus on key regions of the feature map, while the latter may result in the inability to capture information at different scales. In this paper, we propose a novel Attentive Multi-kernel Feature Aggregation (AMFA) Network that incorporates a Synergistic Attention (SA) Module and a Multi-kernel Inception (MI) Module, which effectively addresses the challenges posed by significant variations in target building regions and contextual diversity in cross-view tasks. The SA Module adaptively fuses channel and spatial information to focus on the most discriminative regions within the feature maps. Building upon this, the MI Module extracts multi-scale features through parallel convolutional kernels, enabling a more comprehensive scene representation. Experiments on the University-1652 and SUES-200 datasets show that our method achieves state-of-the-art performance in cross-view geo-localization tasks.

Cross-view geo-localization aims to match the same geographic location from different view images, e.g., drone-view images and geo-referenced satellite-view images. Due to UAV cameras' different shooting angles and heights, the scale of the same captured target building in the drone-view images varies greatly. Meanwhile, there is a difference in size and floor area for different geographic locations in the real world, such as towers and stadiums, which also leads to scale variants of geographic targets in the images. However, existing methods mainly focus on extracting the fine-grained information of the geographic targets or the contextual information of the surrounding area, which overlook the robust feature for scale changes and the importance of feature alignment. In this study, we argue that the key underpinning of this task is to train a network to mine a discriminative representation against scale variants. To this end, we design an effective and novel end-to-end network called Self-Adaptive Feature Extraction Network (Safe-Net) to extract powerful scale-invariant features in a self-adaptive manner. Safe-Net includes a global representation-guided feature alignment module and a saliency-guided feature partition module. The former applies an affine transformation guided by the global feature for adaptive feature alignment. Without extra region annotations, the latter computes saliency distribution for different regions of the image and adopts the saliency information to guide a self-adaptive feature partition on the feature map to learn a visual representation against scale variants. Experiments on two prevailing large-scale aerial-view geo-localization benchmarks, i.e., University-1652 and SUES-200, show that the proposed method achieves state-of-the-art results. In addition, our proposed Safe-Net has a significant scale adaptive capability and can extract robust feature representations for those query images with small target buildings. The source code of this study is available at: https://github.com/AggMan96/Safe-Net.

In this paper, we study the cross-view geo-localization problem to match images from different viewpoints. The key motivation underpinning this task is to learn a discriminative viewpoint-invariant visual representation. Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network. Specifically, we introduce a Unit Subtraction Attention Module (USAM) that can automatically discover representative keypoints from feature maps and draw attention to the salient regions. USAM contains very few learning parameters but yields significant performance improvement and can be easily plugged into different networks. We demonstrate through extensive experiments that (1) by incorporating USAM, RK-Net facilitates end-to-end joint learning without the prerequisite of extra annotations. Representation learning and keypoint detection are two highly-related tasks. Representation learning aids keypoint detection. Keypoint detection, in turn, enriches the model capability against large appearance changes caused by viewpoint variants. (2) USAM is easy to implement and can be integrated with existing methods, further improving the state-of-the-art performance. We achieve competitive geo-localization accuracy on three challenging datasets, i. e., University-1652, CVUSA and CVACT. Our code is available at https://github.com/AggMan96/RK-Net.