hyperquest 0.1.4

A Python package for Hyperspectral quality estimation in hyperspectral imaging (imaging spectroscopy)

HyperQuest

hyperquest: A Python package for estimating image-wide quality estimation metrics of hyperspectral imaging (imaging spectroscopy). Computations are sped up and scale with number of cpus.

Installation Instructions

The latest release can be installed via pip:

pip install hyperquest

All Methods

Result	Method	Description
SNR	hrdsdc()	Homogeneous regions division and spectral de-correlation (Gao et al., 2008)
	rlsd()	Residual-scaled local standard deviation (Gao et al., 2007)
	ssdc()	Spectral and spatial de-correlation (Roger & Arnold, 1996)
Co-Registration	sub_pixel_shift()	Computes sub pixel co-registration between the VNIR & VSWIR imagers using skimage phase_cross_correlation
Smile	smile_metric()	Similar to MATLAB "smileMetric". Computes derivatives of O2 and CO2 absorption features.

Usage example

• see Example Using EMIT for a recent use case.

import hyperquest
import matplotlib.pyplot as plt


# Define path to envi image header file
envi_hdr_path = '/path/my_spectral_image.hdr'

# get wavelengths
wavelengths = hyperquest.read_center_wavelengths(envi_hdr_path)

# compute SNR using HRDSDC method
snr = hyperquest.hrdsdc(envi_hdr_path, n_segments=10000, 
                        compactness=0.1, n_pca=3, ncpus=3)

plt.scatter(wavelengths, snr, color='black', s=100, alpha=0.7)

Citing HyperQuest (STILL WORKING ON THIS, TODO:)

If you use HyperQuest in your research, please use the following BibTeX entry.

@article{wilder202x,
  title={x},
  author={Brenton A. Wilder},
  journal={x},
  url={x},
  year={x}
}

References:

• Cogliati, S., Sarti, F., Chiarantini, L., Cosi, M., Lorusso, R., Lopinto, E., ... & Colombo, R. (2021). The PRISMA imaging spectroscopy mission: overview and first performance analysis. Remote sensing of environment, 262, 112499.

• Curran, P. J., & Dungan, J. L. (1989). Estimation of signal-to-noise: a new procedure applied to AVIRIS data. IEEE Transactions on Geoscience and Remote sensing, 27(5), 620-628.

• Dadon, A., Ben-Dor, E., & Karnieli, A. (2010). Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (smile) in Hyperion images. IEEE Transactions on Geoscience and Remote Sensing, 48(6), 2603-2612.

• Gao, L., Wen, J., & Ran, Q. (2007, November). Residual-scaled local standard deviations method for estimating noise in hyperspectral images. In Mippr 2007: Multispectral Image Processing (Vol. 6787, pp. 290-298). SPIE.

• Gao, L. R., Zhang, B., Zhang, X., Zhang, W. J., & Tong, Q. X. (2008). A new operational method for estimating noise in hyperspectral images. IEEE Geoscience and remote sensing letters, 5(1), 83-87.

• Roger, R. E., & Arnold, J. F. (1996). Reliably estimating the noise in AVIRIS hyperspectral images. International Journal of Remote Sensing, 17(10), 1951-1962.

• Scheffler, D., Hollstein, A., Diedrich, H., Segl, K., & Hostert, P. (2017). AROSICS: An automated and robust open-source image co-registration software for multi-sensor satellite data. Remote sensing, 9(7), 676.

• Tian, W., Zhao, Q., Kan, Z., Long, X., Liu, H., & Cheng, J. (2022). A new method for estimating signal-to-noise ratio in UAV hyperspectral images based on pure pixel extraction. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 399-408.