hyperquest 0.1.13

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. Available methods and summaries can be found in documentation.

Important: this package assumes the following about input hyperspectral data:

• Data must be in NetCDF (.nc) or ENVI (.hdr)
• Currently data is expected in Radiance.
  • For smile & striping methods, data must not be georeferenced (typically referred to as L1B before ortho)
• Pushbroom imaging spectrometer, such as, but not limited to:
  • AVIRIS-NG, AVIRIS-3, DESIS, EnMAP, EMIT, GaoFen-5, HISUI, Hyperion EO-1, HySIS, PRISMA, Tanager-1

NOTE: this is under active development. It is important to note that noise methods shown here do not account for spectrally correlated noise. This is a work in progress as I digest literature and translate into python.

Installation Instructions

The latest release can be installed via pip:

pip install hyperquest

If using Windows PC, you must have "Build Tools" installed to compile cython code,

• Testing on my beat-up Windows PC (Windows11), I did the following to get it to work
  • Installed Visual Studio Build Tools 2022
  • making sure to check the box next to "Desktop development with C++"
  • and then, pip install hyperquest

Usage example

• see EMIT example which has different methods computed over Libya-4.

libRadtran install instructions

• Can be installed on Unix type system using the following link:
  • http://www.libradtran.org/doku.php?id=download

Citation

Brent Wilder. (2025). brentwilder/HyperQuest: v0.XXX (vXXX). Zenodo. https://doi.org/10.5281/zenodo.14890171