Integrating Multiscale Consistency and Enhanced Feature Interaction for Cross-View Geo-Localization

Cross-View Geo-Localization (CVGL) aims to match the correspondence between images of different views captured in the same geographic area. Its key is to mine sufficiently discriminative features in different views to form an invariant representation of the same object location. However, existing methods focus on extracting feature information from a single approach such as coarse-grained, fine-grained, or intermediate layer outputs, while ignoring the sensitivity of multi-scale feature fusion such as channel, space, and training strategy. In this paper, we propose a new enhanced feature interaction and multi-scale scene consistency method, named ECSNet. Firstly, we explicitly bridge positive samples across different batches and aggregate features into a global classifier to promote cross-view invariance and enhance scene separability. Secondly, a Tensor-based Multi-scale Attention (TMA) is designed to utilize the attention support to fuse the multi-scale features of the input image, the intermediate-layer and the edges to maintain the view space details at different granularities. Meanwhile, the Enhanced Feature Interaction (EFI) leverages a visual state space with residual connections to preserve positional offsets to preserve local features and suppresses irrelevant features while selectively zooming in on important details through channel attention. Finally, a regularized Curriculum Learning (CL) strategy is introduced to CL evaluates the magnitude of positional changes and the difficulty of samples, stabilizing optimization while preventing premature overfitting to noisy high-loss noise pairs. Extensive experiments on University-1652 and SUES-200 demonstrate that the method achieves state-of-the-art performance in drone object localization and drone navigation applications, with an improvement of AP more than 3% compared to existing methods.