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   https://doi.org/10.21227/fgb9-qs51  

   Yan, Hua Yan; Zheng, Rachel Zheng; Boehm, Brandon Boehm; Shaga, Sameer Shaga; Black, Derienne Black; Russell, Randy Russell; Kursun, Olcay Kursun (July 2024, IEEE DataPort)

   The CloudPatch-7 Hyperspectral Dataset comprises a manually curated collection of hyperspectral images, focused on pixel classification of atmospheric cloud classes. This labeled dataset features 380 patches, each a 50x50 pixel grid, derived from 28 larger, unlabeled parent images approximately 5000x1500 pixels in size. Captured using the Resonon PIKA XC2 camera, these images span 462 spectral bands from 400 to 1000 nm. Each patch is extracted from a parent image ensuring that its pixels fall within one of seven atmospheric conditions: Dense Dark Cumuliform Cloud, Dense Bright Cumuliform Cloud, Semi-transparent Cumuliform Cloud, Dense Cirroform Cloud, Semi-transparent Cirroform Cloud, Clear Sky - Low Aerosol Scattering (dark), and Clear Sky - Moderate to High Aerosol Scattering (bright). Incorporating contextual information from surrounding pixels enhances pixel classification into these 7 classes, making this dataset a valuable resource for spectral analysis, environmental monitoring, atmospheric science research, and testing machine learning applications that require contextual data. Parent images are very big in size, but they can be made available upon request. 

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3. Ultraviolet Hyperspectral Interferometric Microscopy

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   Ojaghi, Ashkan; Fay, Meredith E.; Lam, Wilbur A.; Robles, Francisco E. (July 2018, Scientific Reports)

   Abstract Ultraviolet (UV) spectroscopy is a powerful tool for quantitative (bio)chemical analysis, but its application to molecular imaging and microscopy has been limited. Here we introduce ultraviolet hyperspectral interferometric (UHI) microscopy, which leverages coherent detection of optical fields to overcome significant challenges associated with UV spectroscopy when applied to molecular imaging. We demonstrate that this method enables quantitative spectral analysis of important endogenous biomolecules with subcellular spatial resolution and sensitivity to nanometer-scaled structures for label-free molecular imaging of live cells. 

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4. Hyperspectral Neutron CT with Material Decomposition

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5. Reduced-cost hyperspectral convolutional neural networks

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   null (Ed.)