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

2026-06-02

AI Summary

This work addresses the lack of paired long-wave infrared (LWIR) hyperspectral imagery and ground-truth temperature–emissivity–texture (TeX) annotations that has hindered learning-based TeX decomposition. To this end, we introduce TeX-1500, the first large-scale, real-world paired dataset comprising 1,522 radiometrically calibrated and wavelength-aligned LWIR hyperspectral images along with their consistently reconstructed TeX ground truth, spanning diverse locations, seasons, times of day, and sensor types. Leveraging this dataset, we propose a wavelength-aware TeX-UNet baseline model and demonstrate its effectiveness on the DARPA IH benchmark as well as zero- and few-shot transfer tasks using FTIR data. Our contribution establishes the first reproducible supervised benchmark for physics-driven thermal perception research.

Abstract

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.

Problem

• temperature-emissivity-texture decomposition
• LWIR hyperspectral imaging
• paired dataset
• supervised learning
• thermal perception

Innovation

• LWIR hyperspectral imaging
• temperature-emissivity-texture decomposition
• paired dataset
• wavelength-aware learning
• thermal perception