# TeX-1500: A Paired Real-World LWIR Hyperspectral Dataset and Benchmark for Temperature-Emissivity-Texture Decomposition

arXiv: https://arxiv.org/abs/2606.03806 · HTML: https://mlsanity.com/paper/2606.03806/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 51 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [SpectralGPT: Spectral Remote Sensing Foundation Model](https://mlsanity.com/paper/2311.07113/) (2023)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion](https://mlsanity.com/paper/2303.06840/) (2023)
- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [SpectralEarth: Training Hyperspectral Foundation Models at Scale](https://mlsanity.com/paper/2408.08447/) (2024)
- [HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images](https://mlsanity.com/paper/2003.04645/) (2020)
- [Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations](https://mlsanity.com/paper/2103.06842/) (2021)
- [PID: Physics-Informed Diffusion Model for Infrared Image Generation](https://mlsanity.com/paper/2407.09299/) (2024)
- [Learning Continuous Wasserstein Barycenter Space for Generalized All-in-One Image Restoration](https://mlsanity.com/paper/2602.23169/) (2026)
- [Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather](https://mlsanity.com/paper/2407.02286/) (2024)
- [ThermalGen: Style-Disentangled Flow-Based Generative Models for RGB-to-Thermal Image Translation](https://mlsanity.com/paper/2509.24878/) (2025)
- [Long-range UAV Thermal Geo-localization with Satellite Imagery](https://mlsanity.com/paper/2306.02994/) (2023)
- [Why are thermal images blurry](https://mlsanity.com/paper/2307.15800/) (2023)
- [Absorption-Based, Passive Range Imaging from Hyperspectral Thermal Measurements](https://mlsanity.com/paper/2308.05818/) (2023)
- [Universal computational thermal imaging overcoming the ghosting effect](https://mlsanity.com/paper/2604.01542/) (2026)
- [HADAR-Based Thermal Infrared Hyperspectral Image Restoration](https://mlsanity.com/paper/2605.13664/) (2026)
- [WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification](https://mlsanity.com/paper/2012.13920/) (2020)
- [PCMamba: Physics-Informed Cross-Modal State Space Model for Dual-Camera Compressive Hyperspectral Imaging](https://mlsanity.com/paper/2505.16373/) (2025)
- [Physics-Integrated Inference for Signal Recovery in Non-Gaussian Regimes](https://mlsanity.com/paper/2601.18074/) (2026)

## Built on by (most cited first, 1 of 1)

- [T2exture: Sparsely Perturbed Thermal-to-Texture Imaging](https://mlsanity.com/paper/2608.02192/) (2026)

## Abstract (arXiv)

Temperature-emissivity-texture (TeX) decomposition seeks to recover object heat state, material spectral response, and visible-like geometric texture from long-wave infrared hyperspectral imaging (LWIR HSI). Existing TeX pipelines are mainly scene-specific inverse solvers, and the lack of paired LWIR HSI-TeX supervision has limited learning-based decomposition. To address this gap, we introduce TeX-1500, a large-scale paired LWIR HSI-TeX dataset and benchmark for supervised HSI-to-TeX decomposition. TeX-1500 contains 1,522 calibrated real-scene pairs from DARPA Invisible Headlights (DARPA IH) pushbroom imagery and our FTIR acquisitions, covering five locations, four seasons, diverse acquisition times, heterogeneous wavelength layouts, and two sensor families. Each sample stores a calibrated valid-band radiance cube, calibrated wavelength positions, and aligned temperature, emissivity, and texture supervision constructed through a consistent restoration and TeX-construction protocol. We further provide TeX-UNet, a simple wavelength-aware baseline that maps calibrated HSI bands and wavelength positions to TeX fields. Experiments on the held-out DARPA IH pushbroom scenes and zero-/few-shot transfer to FTIR scenes show that TeX-1500 provides usable paired supervision and a measurable benchmark for data-driven physical-property-centered thermal perception.