A text-assisted UAV-view geo-localization approach

With the rapid advancement of computer vision technology, cross-view geo-localization has become a crucial research focus, especially in UAV-view geo-localization. Existing methods have made significant progress in addressing viewpoint and altitude discrepancies. However, they frequently overlook environmental variability, which can significantly impact both image quality and the resulting localization accuracy. To address these issues, we introduce a novel text-guided environment-invariant feature learning module, which leverages semantic information from textual environment descriptions to guide the learning of robust and environment-invariant feature representations. Furthermore, we employ an enhanced parallel attention mechanism. This mechanism integrates channel attention and pixel-level attention in a parallel structure to simultaneously capture both global features and fine-grained details. Experimental results on the University-1652 dataset demonstrate the effectiveness of the proposed method, confirming its robustness and adaptability in various environments.