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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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Radiometric sensitivity and resolution of synthetic tracking imaging for orbital debris monitoring.
We consider sampling and detection strategies for solar-illuminated space debris. We argue that the lowest detectable debris cross-sectional area (corresponding to a factor of 3-10x in diameter) may be reduced by 10-100x by analysis of stacks of image frames collected at high rates rather than single frame data. In particular, instead of a pixel as a spatial region, the analysis is based on a phase-space-pixel which corresponds to an angular velocity and space region and whose intensity is computed by a weighted stacking of spatial pixels corresponding to a test debris trajectory within a wide camera field-of-view (FOV). To isolate debris signals from background, the exposure time is set to match the time it takes for debris to transit through the instantaneous field of view. Debris signatures are detected by multiscale X-ray processing of the data cube. Radiometric analysis of line integrals shows that sub-cm objects in Low Earth Orbit can be detected and assigned full orbital parameters by this approach.
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- Award ID(s):
- 2404362
- PAR ID:
- 10667642
- Publisher / Repository:
- Optical Express
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 34
- Issue:
- 3
- ISSN:
- 1094-4087
- Page Range / eLocation ID:
- 4036
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1364/OE.586248
