Cross-view geo-localization, Image retrieval, Multiscale geometric modeling, Frequency domain enhancement

TL;DR

This paper introduces SFDE, a novel neural network architecture that combines spatial and frequency domain features to improve cross-view geo-localization accuracy, especially under challenging viewpoint variations.

Contribution

The paper proposes a three-branch network that models global, local, and frequency-based features, enhancing cross-view image matching for geo-localization tasks.

Findings

SFDE achieves state-of-the-art or competitive results on benchmark datasets.

The frequency domain branch improves robustness to viewpoint changes.

The model is lightweight and computationally efficient.

Abstract

Cross-view geo-localization (CVGL) aims to establish spatial correspondences between images captured from significantly different viewpoints and constitutes a fundamental technique for visual localization in GNSS-denied environments. Nevertheless, CVGL remains challenging due to severe geometric asymmetry, texture inconsistency across imaging domains, and the progressive degradation of discriminative local information. Existing methods predominantly rely on spatial domain feature alignment, which is inherently sensitive to large scale viewpoint variations and local disturbances. To alleviate these limitations, this paper proposes the Spatial and Frequency Domain Enhancement Network (SFDE), which leverages complementary representations from spatial and frequency domains. SFDE adopts a three branch parallel architecture to model global semantic context, local geometric structure, and statistical stability in the frequency domain, respectively, thereby characterizing consistency across domains from the perspectives of scene topology, multiscale structural patterns, and frequency invariance. The resulting complementary features are jointly optimized in a unified embedding space via progressive enhancement and coupled constraints, enabling the learning of cross-view representations with consistency across multiple granularities. Comprehensive experiments show that SFDE achieves competitive performance and in many cases even surpasses state-of-the-art methods, while maintaining a lightweight and computationally efficient design. {Our code is available at https://github.com/Mashuaishuai669/SFDE