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Preliminary analysis of RGB-NIR Image Registration techniques for off-road forestry environments

arXiv cs.CV / 3/13/2026

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Key Points

  • RGB-NIR image registration techniques are evaluated for off-road forestry applications, comparing both classical and deep learning approaches.
  • NeMAR, trained under six configurations, shows partial success but GAN loss instability raises concerns about preserving geometric consistency.
  • MURF demonstrates promising large-scale feature alignment in this context but struggles with preserving fine details in dense vegetation.
  • The study concludes that further refinements are needed for robust, multi-scale registration in off-road forest environments.

Abstract

RGB-NIR image registration plays an important role in sensor-fusion, image enhancement and off-road autonomy. In this work, we evaluate both classical and Deep Learning (DL) based image registration techniques to access their suitability for off-road forestry applications. NeMAR, trained under 6 different configurations, demonstrates partial success however, its GAN loss instability suggests challenges in preserving geometric consistency. MURF, when tested on off-road forestry data shows promising large scale feature alignment during shared information extraction but struggles with fine details in dense vegetation. Even though this is just a preliminary evaluation, our study necessitates further refinements for robust, multi-scale registration for off-road forest applications.

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