Passive LWIR Hyperspectral Ranging via Transmittance Extraction and Distance Alignment

2026-09-23

AI Summary

This study addresses the computational complexity and bias susceptibility inherent in jointly retrieving temperature, emissivity, and range from passive long-wave infrared hyperspectral measurements. We propose the Transmittance Extraction and Distance Alignment (TEDA) framework, which decouples ranging from temperature–emissivity retrieval through log-domain baseline estimation, dual-branch smooth fusion, observation gating, and sensor-domain model matching, thereby eliminating errors introduced by fixed attenuation coefficient approximations. Experimental evaluations on real-world data demonstrate that TEDA achieves superior accuracy compared to existing reference methods while accelerating processing speed by approximately 20-fold. This work effectively resolves the challenge of rapid, high-precision ranging in low-light nighttime scenarios.

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.

Problem

• Passive LWIR hyperspectral ranging
• Joint estimation
• Computational cost
• Ranging bias
• Distance-invariant attenuation coefficient

Innovation

• Passive LWIR hyperspectral ranging
• Transmittance extraction
• Distance alignment
• Decoupled estimation
• Atmospheric absorption