# Absorption-Based, Passive Range Imaging from Hyperspectral Thermal Measurements

arXiv: https://arxiv.org/abs/2308.05818 · HTML: https://mlsanity.com/paper/2308.05818/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 64 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network](https://mlsanity.com/paper/2112.06782/) (2021)
- [Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter](https://mlsanity.com/paper/1804.02836/) (2018)

## Built on by (most cited first, 8 of 8)

- [HADAR-Based Thermal Infrared Hyperspectral Image Restoration](https://mlsanity.com/paper/2605.13664/) (2026)
- [Ozone Cues Mitigate Reflected Downwelling Radiance in LWIR Absorption-Based Ranging](https://mlsanity.com/paper/2603.00273/) (2026)
- [Thermal Polarimetric Multi-view Stereo](https://mlsanity.com/paper/2510.20972/) (2025)
- [TeX-1500: A Paired Real-World LWIR Hyperspectral Dataset and Benchmark for Temperature-Emissivity-Texture Decomposition](https://mlsanity.com/paper/2606.03806/) (2026)
- [Absorption-Feature-Guided Distance-Decoupled Estimation and Band Selection for LWIR Hyperspectral Passive Ranging](https://mlsanity.com/paper/2606.31824/) (2026)
- [Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging](https://mlsanity.com/paper/2606.08324/) (2026)
- [T2exture: Sparsely Perturbed Thermal-to-Texture Imaging](https://mlsanity.com/paper/2608.02192/) (2026)
- [Passive LWIR Hyperspectral Ranging via Transmittance Extraction and Distance Alignment](https://mlsanity.com/paper/2609.28967/) (2026)

## Abstract (arXiv)

Passive hyperspectral longwave infrared measurements are remarkably informative about the surroundings. Remote object material and temperature determine the spectrum of thermal radiance, and range, air temperature, and gas concentrations determine how this spectrum is modified by propagation to the sensor. We introduce a passive range imaging method based on computationally separating these phenomena. Previous methods assume hot and highly emitting objects; ranging is more challenging when objects' temperatures do not deviate greatly from air temperature. Our method jointly estimates range and intrinsic object properties, with explicit consideration of air emission, though reflected light is assumed negligible. Inversion being underdetermined is mitigated by using a parametric model of atmospheric absorption and regularizing for smooth emissivity estimates. To assess where our estimate is likely accurate, we introduce a technique to detect which scene pixels are significantly influenced by reflected downwelling. Monte Carlo simulations demonstrate the importance of regularization, temperature differentials, and availability of many spectral bands. We apply our method to longwave infrared (8--13 μm) hyperspectral image data acquired from natural scenes with no active illumination. Range features from 15m to 150m are recovered, with good qualitative match to lidar data for pixels classified as having negligible reflected downwelling.