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UAV-TIRVis: A Benchmark Dataset for Thermal-Visible Image Registration from Aerial Platforms.

Costin-Emanuel Vasile1, Călin Bîră1, Radu Hobincu1

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Registering Unmanned Aerial Vehicle (UAV)-based thermal and visible images is difficult. A new dataset, UAV-TIRVis, and a heuristic method show improved cross-spectral image alignment performance.

Keywords:
KAZEORBSIFTSURFUAV datasetbenchmark datasetcross-spectral alignmentimage registrationthermal–visible

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Robotics

Background:

  • Unmanned Aerial Vehicle (UAV)-based image registration presents challenges due to spectral appearance differences.
  • A lack of public benchmarks hinders the development and evaluation of cross-spectral registration methods.

Purpose of the Study:

  • Introduce UAV-TIRVis, a novel dataset of registered Unmanned Aerial Vehicle (UAV)-based thermal and visible image pairs.
  • Benchmark existing registration techniques and propose a heuristic method for improved cross-spectral alignment.

Main Methods:

  • Dataset creation: 80 manually registered UAV-based thermal (640 × 512) and visible (4K) image pairs across diverse environments.
  • Benchmarking: Evaluation of feature-based (ORB, SURF, SIFT, KAZE), correlation-based, and intensity-based registration methods.
  • Performance Metrics: Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Normalized Cross-Correlation (NCC).

Main Results:

  • Conventional registration methods show limited generalization across scenes, averaging below 0.6 NCC.
  • The custom heuristic intensity-based method achieved higher performance with 0.77 SSIM and 0.82 NCC.
  • Significant performance gaps highlight the inherent difficulty in cross-spectral Unmanned Aerial Vehicle (UAV) alignment.

Conclusions:

  • The UAV-TIRVis dataset provides a valuable resource for advancing cross-spectral image registration research.
  • Existing registration methods require further optimization to effectively handle the complexities of Unmanned Aerial Vehicle (UAV) data.
  • Future research should focus on developing more robust algorithms for accurate cross-spectral alignment in Unmanned Aerial Vehicle (UAV) applications.