Hyperspectral cameras offer a user the ability to capture more information about a scene than can be done with a standard (RGB) camera. Captured images may have ten or hundreds of bands depending on the hardware used, with each channel of the resultant image describing captured light for a given wavelength. The resultant data is a 'Image Cube' (two dimensions being spatial and the third being spectral). Further processing of this Image Cube allows for accurate scene analysis in a range of applications far beyond that of a ordinary RGB image, for example, the 'spectral signatures' for each pixel can be used to discover the material composition of a scene.

The NICTA Spectral Imaging Development Library can be used to assist in processing for Hyperspectral Imaging applications. The benefits associated with using this software includes increases in accuracy of analysis (for example, by removing effects due to lighting and surface reflection on objects), and the reduction in constraints at the time of capture (for example, aerial imaging could be conducted at a wider variety of times of the day due to lighting compensation and ground-based use can be achieved more practically). An important feature of the library is the ability to perform tasks such as material indexing without the need to calibrate for lighting.

In environments where traditional RGB imaging is now used, Hyperspectral Imaging can provide more flexible and better results. Applications such as food inspection, (fruit sorting or inspecting laboratory samples for bacteria or micro-organisms), agriculture (early detection of diseases in plants before they are visible, automated crop identification and health monitoring), and security (face detection allowing more flexible lighting and the ability to more quickly identify skin variants, accurate object tracking, etc) can all be improved through the use of Hyperspectral Data..

Scyllarus: C++ Hyperspectral Image Processing Library

The Scyllarus C++ API is aimed at developers who want to integrate Hyperspectral Processing into their application, or would like to build an application that makes use of the features of Scyllarus. Scyven, the ‘Scyllarus Visualisation Environment’ is built upon the Scyllarus C++ API, if you want to see the features of the API in action, you can check it out now. The Scyllarus C++ API includes many powerful image processing features and tools targeted at Hyperspectral Imaging applications. With an easy to use modular design, it’s simple to integrate the Scyllarus API into your project.

Features~

• Camera agnostic, compatible with all raw Hyperspectral data;
• Load and Save Hyperspectral Image data in the following formats:
• HDR (ENVI) flat/raw file, BIL, BSQ and BIP formats
• HSZ (Hyperspectral Zip) Find out more..
• Illuminant Recovery using the ‘HRK’ method detailed in [paper reference];
• Automatic Reflectance recovery – no calibration required;
• Spectra based material clustering/segmentation;
• NURBS (Non-Uniform Rational B-Spline) encoding/decoding of spectral signatures and images;
• Fast and low memory overhead Hyperspectral Image manipulation features: Crop, Resize, Remove Bands, Align Bands;
• Streamlined pipeline processing model – adding an image and processing for results takes only a few lines of code;
• Accelerated by Armadillo, BLAS, LAPACK, OpenCV, OpenMP and Boost.

Support/Development:

• Extensive documentation;
• Example programs that use the API;
• Ongoing development – addition of features, performance and memory enhancements, bug-fixes.

Scyllarus C++ Pipeline Flowchart:

The above image depicts the Pipeline object. Purple arrows indicate a flow of data, blue boxes indicate I/O object, and orange boxes are processing functions.

NICTA Hyperspectral Processing Pipeline Flowshart: