CCR: A Counterfactual Causal Reasoning-Based Method for Cross-View Geo-Localization

Cross-view geo-localization seeks to match geographic locations using images from varied sources, including drones and satellites. Interpreting images captured by drones poses significant challenges due to the varying positions and scales resulting from the camera's aerial perspective. Traditional approaches have primarily focused on harnessing contextual cues, which may lead to overfitting. Therefore, it is crucial to find an optimal balance between leveraging contextual details and identifying relevant features. To address this, we introduce a novel method for cross-view geo-localization that employs counterfactual causal reasoning (CCR). This method aims to refine the model's focus, ensuring a balanced emphasis on both the intricate details of the target structure and its broader contextual environment. Our method incorporates an Adaptive Dimension Interaction Block (ADIB), which effectively discerns feature interactions across multiple dimensions, enhanced by counterfactual causal reasoning to improve recognition of target structures and their contexts. In tasks of image-based drone-view target localization and drone navigation, our method achieves superior performance on the University-1652 and SUES-200 benchmark datasets. The code and model files will be made available at https://github.com/Cyberpunk1998/CCR.