hyperquest 0.1.5

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.

Important: this package assumes your hyperspectral data is in ENVI format with a .HDR file.

Installation Instructions

The latest release can be installed via pip:

pip install hyperquest

All Methods

Category	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 (Dadon et al., 2010).
	nodd_o2a()	Similar to method in Felde et al. (2003) to solve for nm shift at O2-A. Requires radiative transfer model run.
Radiative Transfer	run_libradtran()	Runs libRadtran based on user input geometry and atmosphere at 1.0 cm-1. Saves to a .csv file for use in methods requiring radiative transfer.

Usage example

• see SNR example where different SNR methods are computed over Libya-4.
• see Smile example where different smile methods are computed over Libya-4.

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.

• Felde, G. W., Anderson, G. P., Cooley, T. W., Matthew, M. W., Berk, A., & Lee, J. (2003, July). Analysis of Hyperion data with the FLAASH atmospheric correction algorithm. In IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477) (Vol. 1, pp. 90-92). IEEE.

• 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.

• Thompson, D. R., Green, R. O., Bradley, C., Brodrick, P. G., Mahowald, N., Dor, E. B., ... & Zandbergen, S. (2024). On-orbit calibration and performance of the EMIT imaging spectrometer. Remote Sensing of Environment, 303, 113986.