HSI Mars: Hyperspectral Image Analysis for Mars Reconnaissance Orbiter

A Python package for loading, processing, and visualising hyperspectral imaging (HSI) data from the CRISM instrument aboard NASA’s Mars Reconnaissance Orbiter (MRO).

Overview

This package provides tools for working with Martian hyperspectral data:

• 📊 Load and process CRISM hyperspectral images in ENVI format

• 🏷️ Handle ground truth annotations for machine learning

• 🎨 Create false-colour visualisations of spectral data

• 📈 Plot and analyse spectral signatures

• 📉 Generate histograms for spectral band analysis

• ⚡ Work with large datasets through lazy loading

Dataset

This package is designed to work with the dataset published by Xi et al. (2025) in IEEE Geoscience and Remote Sensing Letters.

• Paper: Xi et al., IEEE GRSL, 2025

• Dataset: SciDB Repository

Quick Start

A simple example:

from hsimars import HSIMars

# Load a hyperspectral image
hsi = HSIMars(hdr_path="path/to/your/image.hdr")

# Get image data and metadata
img_data = hsi.get_img()
print(f"Image shape: {img_data.shape}")
print(f"Wavelength range: {img_data.wavelength.min():.1f} - {img_data.wavelength.max():.1f} nm")

# Display the false-colour image
hsi.display_hsi()

# Plot spectrum for a specific pixel
hsi.plot_spectra(px=[100, 200], bands=True)

Working with Annotations

If you have ground truth annotations:

from hsimars import HSIMars

# Load image with annotations
hsi = HSIMars(
    hdr_path="path/to/image.hdr",
    annotations="path/to/labels.mat"
)

# Get both image and annotation data
img_data, ann_data = hsi.data()

# Display image with annotation overlay
hsi.display()  # Shows three panels: image, annotations, and overlay

Table of Contents

Getting Started

• Installation Guide
  • Prerequisites
  • Installation Methods
  • Development Setup
  • Verifying Installation
  • Troubleshooting
  • Next Steps
• Quick Start Guide
  • Basic Usage
  • Working with Annotations
  • Spectral Analysis
  • Complete Example
  • Memory Considerations
  • Best Practices

Tutorials

• Tutorials
  • Getting the Notebooks
  • Prerequisites
  • Tutorial Notebooks
  • Dataset Information
  • Troubleshooting

API Reference

• HSIMars
  • hsimars package
    • HSI Mars: Hyperspectral Image Analysis for Mars Reconnaissance Orbiter Data
    • HSIMars

Additional Resources

• Advanced Examples
• Contributing Guide

Key Features

Memory-Efficient Processing

The package uses lazy loading for memory efficiency when working with large hyperspectral datasets. Data loads from disk only when first accessed, then caches for later use.

Spectral Analysis Tools

• Spectral Signatures: Plot individual or averaged spectra with optional convex hull removal for continuum normalisation

• Band Visualisation: Overlay spectral band regions (VIS, NIR, SWIR, MWIR) on plots

• Statistical Analysis: Generate histograms for analysing spectral band distributions

Visualisation Capabilities

• False-Colour Images: Render HSI data using representative spectral bands

• Annotation Overlays: Display ground truth labels with colour-coded classes

• Interactive Display: OpenCV-based interactive windows for data exploration

Contributing

Contributions are welcome. To contribute:

1. Fork the repository on GitHub

2. Create a feature branch (git checkout -b feature/new-feature)

3. Make your changes

4. Run tests (pytest tests/)

5. Commit your changes (git commit -m 'Add new feature')

6. Push to your fork (git push origin feature/new-feature)

7. Open a pull request

Ensure your code follows the existing style and includes tests and documentation.

Licence

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.

Indices and Tables

• Index

• Module Index

• Search Page