Passive infrared method speeds up and improves distance estimation at night
Passive LWIR Hyperspectral Ranging via Transmittance Extraction and Distance Alignment
Computer Vision and Pattern Recognition
Summary
Measuring distances at night or in low light is hard using normal cameras because there is little visible light. This paper presents a new method that uses heat radiation and the way the atmosphere absorbs it to estimate how far away objects are. The authors separate the problem into two steps: first, they extract the atmospheric absorption pattern from the thermal data, and then they match that pattern to distance models. Their method is faster and more accurate than previous techniques based on the same idea.
What this means in practice
- •For autonomous vehicle engineers: Enhance night-time distance sensing by integrating faster passive thermal ranging to complement or verify LiDAR measurements.
- •For security system developers: Improve perimeter intrusion detection in low-light conditions without active illumination by using passive LWIR hyperspectral distance estimates.
Authors
Zhihe Chen, Chen Fan, Shuo Liu, Xiaolin Huang, Yunze He, Xiaofeng He, Lilian Zhang
Abstract
Passive long-wave infrared (LWIR) hyperspectral ranging enables distance estimation in low-light and nighttime scenes by exploiting atmospheric absorption features in thermal radiance received through the atmosphere.Joint estimation of temperature, emissivity, and distance is computationally expensive. Reference-range joint inversion also uses a distance-invariant effective attenuation coefficient, which can bias range estimates.We introduce transmittance extraction and distance alignment (TEDA), which decouples range estimation from temperature--emissivity inversion. In the first stage, a baseline estimator with a data-fidelity term invariant to the known absorption direction yields two closed-form smoothing branches for the slowly varying thermal continuum. An observation-derived gate combines the branches, and subtracting the blended baseline in the log domain recovers atmospheric transmittance. The second stage estimates range by matching the recovered transmittance to sensor-domain transmittance models recomputed for each candidate distance. Monte Carlo simulations show that TEDA effectively reduces the ranging bias caused by the distance-invariant attenuation coefficient approximation. In a measured scene, TEDA's mean range estimates are closer to the LiDAR medians than those of reference-range joint inversion in both evaluated patches. TEDA processes a complete $256\times256$ region of interest in 8.19~s versus 159.47~s for reference-range joint inversion, an approximately 20-fold speedup.