GRSG has  brought together a collection of powerful, free geospatial software and programming libraries to address a comprehensive remote sensing applications.

These tools support advanced image processing techniques across optical, multispectral, SAR (Synthetic Aperture Radar), and hyperspectral datasets – enabling professionals and researchers to analyse, classify, and visualize Earth observation data with precision.

OSGeo (all downloadable as a bundle of packages for windows through OsGeo4W):

• • QGIS
    • EnMAP-box plugin (QGIS plugin developed by GFZ and useful for EnMAP, EMIT, PRISMA and other hyperspectral data analysis)
    • QGIS Semi-Automatic Classification Plugin (SCP) – (mainly for multispectral data)
  • SAGA GIS – ideal for image classification and terrain tools
  • GRASS GIS – image preprocessing/PCA/classification/modeling

ESA Toolboxes:

• • PolSARPro – Tool for applications using radar polarimetry
  • Sentinel-Toolbox/SNAP (for optical multispectral and SAR image processing)

Hyperspectral Python (HypPy)

A software package developed by the Geological Remote Sensing group of the ITC-University of Twente. It is based on Python and used for spectral analysis. For more information please visit the following websites:

• • • General information on the software package: https://www.itc.nl/about-itc/scientific-departments/applied-earth-sciences/research/geological-remote-sensing/
    • Github page to download the latest version: https://github.com/wimhbakker/hyppy
    • Scientific paper regarding the toolbox: https://doi.org/10.3390/a17080337

Software that involve coding skills:

• R-project (R-Studio as interface) – It is a free software for statistical computing, but has several packages for raster and image processing (here is a link to an online guide on remote sensing analysis using R: https://rspatial.org/rs/rs.pdf)
• Scilab: It is an open-source software for numerical computation https://gitlab.com/scilab/scilab/
• Google Earth Engine (not open source but free only for non-commercial purposes): it is a cloud catalog of satellite imagery and geospatial datasets on a global scale and allows to perform multitemporal and statistical analyses. Coding is carried out in javascript.
• Python: is a programming language containing several packages useful for image processing and geospatial data analysis: e.g. GDAL, Scikit-learn (machine learning and AI), SciPy, Numpy, SpectralPy, Pandas. Here is a reference for getting started: https://www.earthdatascience.org/tags/remote-sensing/