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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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CloudRadianceHSI
This dataset includes 30 hyperspectral cloud images captured during the Summer and Fall of 2022 at Auburn University at Montgomery, Alabama, USA (Latitude N, Longitude W) using aResonon Pika XC2 Hyperspectral Imaging Camera. Utilizing the Spectronon software, the images were recorded with integration times between 9.0-12.0 ms, a frame rate of approximately 45 Hz, and a scan rate of 0.93 degrees per second. The images are calibrated to give spectral radiance in microflicks at 462 spectral bands in the 400 – 1000 nm wavelength region with a spectral resolution of 1.9 nm. A 17 m focal length objective lens was used giving a field of view equal to 30.8 degrees and an integration field of view of 0.71 mrad. These settings enable detailed spectral analysis of both dynamic cloud formations and clear sky conditions. Funded by NSF grant 2003740, this dataset is designed to advance understanding of diffuse solar radiation as influenced by cloud coverage. The dataset is organized into 30 folders, each containing a hyperspectral image file (.bip), a header file (.hdr) with metadata, and an RGB render for visual inspection. Additional metadata, including date, time, central pixel azimuth, and altitude, are cataloged in an accompanying MS Excel file. A custom Python program is also provided to facilitate the reading and display of the HSI files. The images can also be read and analyzed using the free version of the Spectron software available at https://resonon.com/software. To enrich this dataset, we have added a supplementary ZIP file containing multispectral (4-channel) image versions of the original hyperspectral scenes, together with the corresponding per-pixel photon flux and spectral radiance values computed from the full spectrum. These additions extend the dataset’s utility for machine learning and data fusion research by enabling comparative analysis between reduced-band multispectral imagery and full-spectrum hyperspectral data. The ExpandAI Challenge task is to develop models capable of predicting photon flux and radiance—derived from all 462 hyperspectral bands—using only the four multispectral channels. This benchmark aims to stimulate innovation in spectral information recovery, spectral-spatial inference, and physically informed deep learning for atmospheric imaging applications.
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- PAR ID:
- 10527258
- Publisher / Repository:
- IEEE DataPort
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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