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MRGeo: Robust Cross-View Geo-Localization of Corrupted Images via Spatial and Channel Feature Enhancement

Le Wu, Lv Bo, Songsong Ouyang, Yingying Zhu

Abstract

Cross-view geo-localization (CVGL) aims to accurately localize street-view images through retrieval of corresponding geo-tagged satellite images. While prior works have achieved nearly perfect performance on certain standard datasets, their robustness in real-world corrupted environments remains under-explored. This oversight causes severe performance degradation or failure when images are affected by corruption such as blur or weather, significantly limiting practical deployment. To address this critical gap, we introduce MRGeo, the first systematic method designed for robust CVGL under corruption. MRGeo employs a hierarchical defense strategy that enhances the intrinsic quality of features and then enforces a robust geometric prior. Its core is the Spatial-Channel Enhancement Block, which contains: (1) a Spatial Adaptive Representation Module that models global and local features in parallel and uses a dynamic gating mechanism to arbitrate their fusion based on feature reliability; and (2) a Channel Calibration Module that performs compensatory adjustments by modeling multi-granularity channel dependencies to counteract information loss. To prevent spatial misalignment under severe corruption, a Region-level Geometric Alignment Module imposes a geometric structure on the final descriptors, ensuring coarse-grained consistency. Comprehensive experiments on both robustness benchmark and standard datasets demonstrate that MRGeo not only achieves an average R@1 improvement of 2.92\% across three comprehensive robustness benchmarks (CVUSA-C-ALL, CVACT\_val-C-ALL, and CVACT\_test-C-ALL) but also establishes superior performance in cross-area evaluation, thereby demonstrating its robustness and generalization capability.

MRGeo: Robust Cross-View Geo-Localization of Corrupted Images via Spatial and Channel Feature Enhancement

Abstract

Cross-view geo-localization (CVGL) aims to accurately localize street-view images through retrieval of corresponding geo-tagged satellite images. While prior works have achieved nearly perfect performance on certain standard datasets, their robustness in real-world corrupted environments remains under-explored. This oversight causes severe performance degradation or failure when images are affected by corruption such as blur or weather, significantly limiting practical deployment. To address this critical gap, we introduce MRGeo, the first systematic method designed for robust CVGL under corruption. MRGeo employs a hierarchical defense strategy that enhances the intrinsic quality of features and then enforces a robust geometric prior. Its core is the Spatial-Channel Enhancement Block, which contains: (1) a Spatial Adaptive Representation Module that models global and local features in parallel and uses a dynamic gating mechanism to arbitrate their fusion based on feature reliability; and (2) a Channel Calibration Module that performs compensatory adjustments by modeling multi-granularity channel dependencies to counteract information loss. To prevent spatial misalignment under severe corruption, a Region-level Geometric Alignment Module imposes a geometric structure on the final descriptors, ensuring coarse-grained consistency. Comprehensive experiments on both robustness benchmark and standard datasets demonstrate that MRGeo not only achieves an average R@1 improvement of 2.92\% across three comprehensive robustness benchmarks (CVUSA-C-ALL, CVACT\_val-C-ALL, and CVACT\_test-C-ALL) but also establishes superior performance in cross-area evaluation, thereby demonstrating its robustness and generalization capability.
Paper Structure (25 sections, 8 equations, 4 figures, 4 tables)

This paper contains 25 sections, 8 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Cross-View Geo-Localization
  4. Corrupted Image Robustness in CVGL
  5. Method
  6. Problem Definition
  7. Spatial Adaptive Representation Module
  8. Channel Calibration Module
  9. Region-level Geometric Alignment Module
  10. Optimization Objective
  11. Experiment
  12. Datasets and Evaluation Metrics
  13. Implementation Details
  14. Comparison with Existing Methods
  15. Comprehensive Corruption Robustness Evaluation.
  16. ...and 10 more sections

Figures (4)

  • Figure 1: The fragility of existing CVGL models under real-world corruptions. While a model can easily match a clean street-view image to its correct satellite counterpart (top), its performance collapses when the query is affected by common corruptions like weather or blur, often leading to a complete failure in localization (bottom).
  • Figure 2: Overview of our MRGeo architecture. The framework processes street-view and satellite images through a shared-weight backbone containing our proposed Spatial-Channel Enhancement Block (SCEB). SCEB enhances feature quality via its two sub-modules: the SARM and the CCM. Finally, the Region-level Geometric Alignment Module (RGAM) imposes a structural constraint on the enhanced features to generate robust final descriptors for retrieval.
  • Figure 3: Few-shot training on CVUSA. Performance evaluation on the raw test set with progressively sampled training subsets (20%, 40%, 60%, 80%, 100%). The red dotted line is MRGeo's R@1 benchmark on 20% data.
  • Figure 4: Heatmap visualization of MRGeo's feature focus on both clean and corrupted images. Best viewed on screen with zoom-in.