 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, processing speed, and computational complexity have only recently been overcome for many real-world applications. Moreover, the market is understandably skeptical, as production facilities are not the place for incomplete technology. As the technology continues to improve, and as the market comes to understand the capabilities of hyperspectral imaging, previously intractable problems will be solved on large scales. Thus, the future of this exciting technology appears bright.