mvos-hsi 0.2.1

Hyperspectral imagery (HSI) preprocessing utilities for Machine Learning applications

mvos_hsi

MVOS Hyperspectral Imaging Utilities

mvos_hsi is a Python package developed at the
Machine Vision and Optical Sensors (MVOS) Lab, South Dakota State University (SDSU)
for preprocessing and managing hyperspectral imaging (HSI) data.

The package provides a reproducible pipeline for:

• Calibrating raw hyperspectral cubes
• Clipping sample regions (e.g., leaves)
• Applying data augmentation
• Generating graphs for spectral comparisons
• Organizing outputs into clean, ML-ready datasets

It was originally designed for leaf-level agricultural experiments (e.g., nitrogen status in corn leaves) but is broadly applicable to other hyperspectral imaging tasks.

---

Features

• Calibration

  • Single image or batch folder calibration
  • Dark and white reference correction
  • Spectral binning & spatial binning options
  • Outputs calibrated reflectance and fluorescence cubes
  • Saves calibrated cubes as .mat files (*_R.mat, *_F.mat)

• Clipping

  • Detect leaves using vegetation indices:
    • NDVI (Normalized Difference Vegetation Index)
    • CI-RedEdge (Chlorophyll Index Red-Edge)
    • GCI (Green Chlorophyll Index)
  • Thresholding via Otsu (auto) or manual (e.g., NDVI > 0.45)
  • Flexible cropping:
    • Square windows (fixed size, e.g., 30×30 pixels)
    • Tight bounding boxes fit to each leaf
  • Saves clipped cubes as ENVI files (.hdr + .img) in clipped_hypercubes/

• Augmentation

  • Apply data augmentation to calibrated or clipped cubes
  • Options:
    • Per image (single .mat or .hdr)
    • Per folder
    • Per class (each subfolder = class label)
  • User-defined number of augmentations
  • Saves augmented cubes in augmented_hypercubes/

---

Installation

From PyPI

pip install mvos_hsi