A high resolution image is critical for remote sensing applications. By combining the visible and infrared bands, analysts can accurately differentiate the spectral signatures detected in the images. Adapting the approach to each mission allows to reveal the desired signatures for each application. Although there are many ways to assign colors to represent different regions of the spectrum of light, experience shows that some spectral bands have proven more useful than others. One of the most valuable regions of the light spectrum is the near infrared (NIR) band, which allows us to perform vegetation analysis based on the reflectance of the infrared band compared to visible light. As a plant matures or is subjected to stress from disease, insect infestation, or a lack of moisture, the infrared reflectance of the leaf changes and becomes more noticeable to the naked eye. At visible wavelengths, this change is virtually undetectable.
With our 4-band camera we can obtain RGB photographs in addition to the infrared band and thus be able to calculate the Normalized Difference Vegetation Index (NDVI). By measuring the difference in reflectance between near infrared light (which is strongly reflected) and red light (which is absorbed), the health of vegetation can be accurately and quickly quantified and evaluated over a large area.
Another application of NIR imaging is known as supervised classification, where samples from different previously identified areas are used.
To classify pixels of unknown identity, allowing analysts to assign unclassified pixels to one of several informational classes.