What is Hyperspectral Imaging?
Hyperspectral imaging creates digital images
with much higher spectral, or color, resolution than traditional color imaging.
If you are new to the concept of hyperspectral imaging, you might want to read
our Introduction to Hyperspectral Imaging. A hyperspectral
datacube (the 3-dimensional image file that is created by a hyperspectral
camera) provides a detailed spectral curve for every pixel in the image. The high
spectral resolution data enables differentiation between materials more
accurately than is possible with color images, expanding possibilities in a wide range of
applications (e.g., environmental monitoring, precision farming, sorting,
quality control, diagnostics, etc.). Hyperspectral Image Acquisition Using Spectronon™ Software
To work with the large quantity of
hyperspectral data contained within a datacube, Resonon developed a proprietary software called
Spectronon™, a hyperspectral imaging software platform for hyperspectral image acquisition, calibration, visualization, spectral analysis, and hyperspectral imaging analysis across
laboratory, industrial, airborne, and outdoor imaging applications. Since Spectronon’s creation in 2005, we have been continually refining, improving, and enhancing its capabilities. Since that time, many thousands of people have downloaded Spectronon™.
Spectronon™, along with sample datacubes, is available for
free download. We provide these to everyone because we believe that access to hyperspectral imaging data and software is needed to understand and appreciate the
capabilities of spectral imaging.
Spectral Data Visualization & Image Exploration
Spectronon™, along with other built-in tools for hyperspectral imaging analysis, allows users to classify materials, compare spectral signatures, generate reflectance data, and perform advanced spectral analysis workflows. For
example, Spectronon™ makes it easy to see the spectrum of a single pixel or a
user-selected group of pixels.
Below, in
Figure 1, the small gray boxes inside the green and blue circles indicate selections
of pixels from two different types of candy that are nearly identical to the
human eye. The mean value and standard deviation of the spectra from these
pixels are plotted in green and blue in the upper right-hand corner of the Spectronon™
display, as indicated by the green and blue arrows.
That plot (enlarged
in Figure 2) displays the wavelength vs. the signal of the pixels selected. One
can easily see the spectral difference between the two types of red candies.
That plot (enlarged
in Figure 2) displays the wavelength vs. the signal of the pixels selected. One
can easily see the spectral difference between the two types of red candies.
Spectral Analysis Tools for Hyperspectral Data
Spectronon™ comes with many standard
algorithms for classification and regression of hyperspectral data. Classification might be used to determine
a good product from a bad product. Regression is best suited to monitor
changes, such as how much an item has dried or how long it has been cooked.
Figure 3, below, shows an example
classification map of hyperspectral imaging data using an algorithm known as the
Spectral Angle Mapper (SAM). This algorithm compares
every pixel in the image to the blue and green spectra shown in the plot.
Pixels with spectra that are sufficiently similar to the blue spectrum are
colored blue, and those that are sufficiently similar to the green spectrum are
colored green. Pixels that are not similar to either spectrum are colored
black.
The classification map clearly shows that there are two types of red
candy, with one type organized in the shape of an “I.” None of the yellow candy pixels have spectra similar to either type
of red candy, so pixels of the yellow candy are colored black. The thresholds
for similarity in the SAM algorithm are easily adjusted with the sliders
circled in red in Figure 3. Results from other built-in algorithms (Logistic
Regression, Random Forest, Euclidean distance, to name a few) can be quickly
compared to determine which algorithm provides the best results for your
particular application.
Easy to Use and Customizable
Spectronon™ is designed for ease of use.
There is a
quick start guide to get the user up and running. There is also a comprehensive
user manual that includes clear descriptions of everything from basic to advanced capabilities
and the details behind all the algorithms. Introductory tutorial videos to get you up and running are available on our
Library page.
Spectronon™ has the ability to perform batch
processing and data pre-processing steps. The user can also write their own plugins
(in Python), essential for those looking to combine their own analysis algorithms
with the power of Spectronon™. Spectral plots can be saved as text files for
easy import into Excel, Python, or similar software for further analysis. Images can be
easily exported into many different file formats.
Getting Started with Spectronon™
Whether processing laboratory, airborne, or field-collected datasets, Spectronon™ provides the tools needed for efficient hyperspectral imaging analysis, calibration, visualization, and spectral analysis across a wide range of scientific and industrial applications.