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New TeX-1500 dataset advances hyperspectral imaging analysis

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced TeX-1500, a new dataset designed to advance temperature-emissivity-texture (TeX) decomposition from long-wave infrared hyperspectral imaging (LWIR HSI). This dataset comprises over 1,500 paired real-world scenes, bridging the gap in supervised learning for TeX decomposition. It includes calibrated radiance cubes, wavelength positions, and aligned temperature, emissivity, and texture data, along with a baseline model called TeX-UNet. AI

IMPACT Enables more robust learning-based decomposition of thermal and material properties from hyperspectral imagery.

RANK_REASON The cluster contains a research paper introducing a new dataset and benchmark for a specific computer vision task.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Cheng Dai, Jiale Lin, Hongyi Xu, Bingxuan Song, Ziyang Xie, Fanglin Bao ·

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

    arXiv:2606.03806v1 Announce Type: new 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 a…

  2. arXiv cs.CV TIER_1 English(EN) · Fanglin Bao ·

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

    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 th…

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