ow, and prone to mistakes.
Not only can hyperspectral data be used to distinguish differences between similarly colored materials, they can even access information outside the visible range — both infrared and ultraviolet. Real-time machine learning algorithms can process the data in seconds and then transmit the information to actuators such as robotic arms, air-jets, and mechanical flippers to complete the sort.
While designing and manufacturing the cameras is technically challenging, producing reliable hyperspectral analysis software for industrial sorting is both a barrier and an opportunity. Because widely used simple algorithms exist alongside a wealth of knowledge about deep machine learning, hyperspectral machine vision software development is an exciting field.
Traditional methods of analyzing spectral imaging data use the concept of “vegetation indices” or more generally just “indices,” colloquially called “band math”. These indices are scalar quantities associated with each pixel, calculated via simple algebraic combinations of measured intensities of a few specific spectral bands. For example, a historically venerable vegetation index is the Normalized Difference Vegetation Index (NDVI), which dates from the early days of multispectral remote sensing. Given a full reflectance spectrum at each pixel, the value of NDVI can be calculated at each pixel as
where “NIR” is the reflectance at an infrared band (around 750 nm), “Red” is the reflectance of a red band (around 650 nm), and NDVI is a number between -1.0 and 1.0. The magnitude of NDVI is a rough indicator of plant health. NDVI was well suited to early multispectral cameras that recorded four broad-band data points at each pixel (usually blue, green, red, and near-infrared). This is what was measured in the early Landsat satellites.
NDVI can give, at best, a rough estimate of plant health. Since the advent of airborne spectrometers and hyperspectral sensors, many more precise indices have been invented, with new indices invented regularly. An example of a common vegetative index that measures chlorophyll, the Modified Chlorophyll Absorption Reflectance Index (MCARI), takes the form
while an index that measures anthocy-anins in plants, the Anthocyanin Reflectance Index #2 (ARI2) takes the form:
In these equations, ρx is the measured reflectance at wavelength x.
Figure 3 shows a map of MCARI for the farmland in Figure 1. Bright locations in the map indicate high values of MCARI, while dark regions indicate low values. The interpretation of MCARI is that higher values imply higher chlorophyll activity.
Originally developed for agriculture and environmental monitoring, indices have now expanded to other fields, such as water quality, biology, and geology. In medicine, indices related to water and oxygen are used to determine properties of human muscle tissue during surgery.
Although indices are useful tools, they only incorporate a very small portion of the information available in broad continuous spectra. Hyperspectral data carries much more information than a few selected spectral bands and can yield all indices upon request. However, employing the entire spectrum along with statistical methods yields much more reliable classifications — this is exactly what is used in advanced machine vision applications.
Machine learning algorithms that utilize the entire spectrum along with rigorous statistics can more accurately classify plants and other materials. Techniques such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) have proven successful in hyperspectral sorting applications. When combined with spatial recognition algorithms, hyperspectral machine vision can make for very smart computers with very sensitive eyes. This allows assembly-line machines to identify a wide range of materials, patterns, coatings, defects, and contaminants.
A Bright Future
Despite the clear advantage of providing more detailed data than conventional imaging systems, hyperspectral imaging is still in its infancy. Speed limitations for data acquisition, processi