license: unknown
Description
This is a database of fifty hyperspectral images of indoor and outdoor scenes under daylight illumination, and an additional twenty-five images under artificial and mixed illumination. The images were captured using a commercial hyperspectral camera (Nuance FX, CRI Inc) with an integrated liquid crystal tunable filter capable of acquiring a hyperspectral image by sequentially tuning the filter through a series of thirty-one narrow wavelength bands, each with approximately 10nm bandwidth and centered at steps of 10nm from 420nm to 720nm. The camera is equipped with an apo-chromatic lens and the images were captured with the smallest viable aperture setting, thus largely avoiding chromatic aberration. All the images are of static scenes, with labels to mask out regions with movement during exposure.
Characteristics
This real-world hyperspectral images database is being made available for non-commercial research use. Please see the README.txt file in each archive for details. It contains:
CZ_hsdb: 50 Indoor & outdoor images under daylight (5.3GB)CZ_hsdbi: 27 Indoor images under artificial & mixed illumination (2.2GB)
Credits
Originally available at: https://vision.seas.harvard.edu/hyperspec/download.html
This database is available for non-commercial research use. The data is available as a series of MATLAB .mat files (one for each image) containing both the images data and masks. Since the size of the download is large (around 5.5 + 2.2 GB), the authors ask only minimize the number of times you download it directly from those servers.
If you use this data in an academic publication, kindly cite the following paper:
Ayan Chakrabarti and Todd Zickler, "Statistics of Real-World Hyperspectral Images," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
Bibtex:
@conference{chakrabarti2011statistics,
title={{Statistics of Real-World Hyperspectral Images}},
author={Chakrabarti, A. and Zickler, T.},
booktitle={Proc.~IEEE Conf.~on Computer Vision and Pattern Recognition (CVPR)},
pages={193--200},
year={2011}
}