Common tools
You don't need to buy anything to work with Wyvern data. Four of the six tools below are free and open source, and our tutorials cover QGIS, Python, ArcGIS Pro, and ENVI. If you're starting from scratch, pick QGIS if you'd rather not write code and Python if you would.
QGIS
Free
GUI
Open Source
QGIS is free, runs on Windows, macOS, and Linux, and with the EnMAP-Box plugin it handles hyperspectral cubes without any code: spectral profiles, band math, and dimensionality reduction. Most of our no-code tutorials use it. It reads and writes nearly every raster and vector format through GDAL, and its plugin library covers most things the core application doesn't.
ArcGIS
Paid
GUI
Closed Source
ArcGIS is Esri's commercial GIS platform: ArcGIS Pro on the desktop, and ArcGIS Online and ArcGIS Enterprise for hosting and sharing. It's the standard in many government agencies and large organizations, so if your team already has licences, it's often the path of least resistance. Our ArcGIS Pro tutorial covers loading a Wyvern scene.
ENVI
Paid
GUI
Closed Source
ENVI, from NV5 Geospatial, is built specifically for spectral imagery. It has the deepest set of hyperspectral tools of anything on this page, including atmospheric correction (QUAC and FLAASH), spectral libraries, target detection, and spectral unmixing, all through a graphical interface. It's scriptable through IDL and Python, and it's a paid product. Our ENVI tutorial takes a Dragonette scene from import through atmospheric correction to spectral analysis.
Python
Free
Code
Open Source
Python is what most of our tutorials use, and it's the most flexible option on this page. A Wyvern scene loads into a NumPy array with rasterio, band math is one line of NumPy, and xarray keeps wavelengths attached to the data. The same code runs on a laptop, in a notebook on Google Colab, or in a pipeline. The Python tutorials landing page describes the packages we rely on.
R
Free
Code
Open Source
R is a language built for statistics, and it's a natural choice if your analysis ends in a model or a hypothesis test rather than a map. The current packages are terra for rasters, sf for vectors, and stars for data cubes. Older tutorials often use sp, rgdal, rgeos, and raster; rgdal and rgeos were archived from CRAN in October 2023, so start new work with terra, sf, and stars.
GDAL
Free
Code
Open Source
GDAL (Geospatial Data Abstraction Library) is the library underneath most of the other tools here: QGIS, rasterio, and R's terra and sf all use it to read and write files. You can also use it directly. gdalinfo prints a scene's size, projection, and per-band wavelength tags, gdal_translate converts between formats and extracts bands, and gdalwarp reprojects and clips. It's written in C and C++, with bindings for Python and other languages.