Most scientific applications of near infrared imaging focus on near infrared spectroscopy, wherein near infrared light is employed to discover spectral characteristics of test objects. There are two predominant modes: reflective and transmissive (or equivalently absorptive). Unfortunately, the nomenclature is not standardized and the typical acronyms NIR and NIRS can describe either modality.
The reflective modality is the one used in our own experiments. To characterize a scene, it images the reflected source light coming off the constituent objects, often over a narrowed wavelength band. The absorption of near infrared radiation by different objects is highly dependent on molecular structure and chemistry; any energy left after this absorption is simply reflected off the object. This reflected light is captured by the sensor. The hope, naturally, is that the objects can be more easily distinguished by their near infrared than in standard visual reflectance. The absorption modality, on the other hand, simply exploits the absorption directly. Near infrared light is transmitted through a medium and received by a sensor at the far end. The amount of light absorption is measured. Since different compounds in the optical path will tend to absorb different wavelengths, the chemical makeup of the medium can be ascertained. A general introduction to infrared absorption spectroscopy can be found here. In either case, typically organic functional groups are detected, and in controlled cases can be ascertained to 0.1% of their concentration.
It is obvious that the reflective modality is more applicable in situations where the source light is not easily oriented with respect to the test object. An example is aerial foliage studies, where the source light is the sun. Conversely, the absorptive modality finds more application when the source light is more easily oriented, such as in breast cancer imaging. Further description of applications in forestry and biomedicine are found through the links below. Some additional information on the use of near infrared imaging for eye detection algorithms is found in the human eye results section.
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