A Hyperspectral Visualisation Tool Powered by Scyllarus
Scyven (Scyllarus Visualisation Environment) allows you to inspect Hyperspectral images, and analyse images to discover the spectral signatures that are present within the image. Scyven provides access the powerful functionality of the Scyllarus C++ API through a simple Graphical User Interface.
Scyven can be used to learn things about scenes that you can’t discover with the naked eye alone, or with traditional RGB images. Advanced processing techniques allow you to make the most of your Hyperspectral image data. If you don’t have a Hyperspectral camera, you can get some data from us to get started and see what Scyven can do!
Scyven works on both Windows and Linux, and doesn’t require any special hardware to perform data processing – you can capture your data elsewhere and import it into Scyven when you want to analyse it.
- Simple one-click workflow: process Hyperspectral data using Scyllarus in one click:
- Automatic Illuminant Recovery;
- Automatic Reflectance recovery – no calibration required;
- Automatic Material Discovery;
- Classification to Spectral Library using SAM (Spectral Angle Mapping), SVM (Support Vector Machines), and LSU (Linear Spectral Unmixing) algorithms;
- Principal Component Analysis;
- View and compare multiple Hyperspectral images simultaneously;
- Powerful visualisation features – plot pixel or region spectra, view classes and clusters as layers;
- Import endmember/spectral libraries using the Spectral Library Manager;
- Build Spectral Libraries using polygon annotation and Spectral Library Manager tools;
- Camera agnostic, compatible with all raw Hyperspectral data;
- Load and save portable Workspaces – pick up exactly where you left off or share Workspaces;
- Load and Save Hyperspectral Image data in the following formats;
- Tutorial and Userguide;
- Ongoing development – addition of features, performance and memory enhancements, bug-fixes.
- If you require custom functionality or camera integration, please contact us.
Get Scyven (version 1.3.0)
If you use Scyven please cite the paper:
N. Habili and J. Oorloff, Scyllarus: From Research to Commercial Software, In Proceedings of the ASWEC 2015 24th Australasian Software Engineering Conference, Adelaide, Australia, pp 119-122, September 2015.
- Windows 7, 8 or 10, 64-bit, or Ubuntu 16.04 64-bit
- CPU: Multi-core processor recommended
- RAM: Minimum 8GB, 16GB+ recommended for large images