Structure-Aware Matching for Robust UAV Thermal-to-Satellite Geo-Localization

Key points

• While existing approaches predominantly address global modality shifts, they overlook the invariant scene structures inherently shared between thermal and satellite views.1
• To bridge this gap, we propose SAMF, a Structure-Aware Matching Framework that explicitly exploits stable cross-modal structural cues to improve UAV thermal geolocalization.1
• Specifically, the Directional Structure Enhancement Module (DSEM) strengthens robust structural representations by capturing directional and low-frequency spatial patterns, and the Modality-Aware Affine Remapping Module (MARM) subsequently applies affine correction to these enhanced features, further promoting cross-modal alignment.1
• Additionally, we introduce the Thermal-UAV-Paired benchmark derived from the Thermal-UAV dataset.1

Sources (1)

• [1]Structure-Aware Matching for Robust UAV Thermal-to-Satellite Geo-Localization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 12:38 AM

  UAV thermal-to-satellite geo-localization localizes UAVs by matching real-time thermal images against georeferenced satellite images, enabling autonomous navigation in GNSS-denied and low-visibility conditions.

  While existing approaches predominantly address global modality shifts, they overlook the invariant scene structures inherently shared between thermal and satellite views.

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