The benefits of spectral imaging are significant and wide reaching. With the advancement of hardware technologies, image analysis methods, and computational power, I expect spectral imaging to play an important role in numerous applications above and beyond the few examples mentioned here. The power of spectral imaging to detect objects that the human eye, or ordinary RGB machine vision cameras can miss cannot be understated. Single point spectroscopic analysis still plays a vital role, but multispectral and hyperspectral imaging offers an exciting glimpse into a healthier and happier future.
Explanation:
Optical spectroscopy provides an invaluable insight into the interaction between light and matter and is used in a remarkable range of applications. Traditionally, spectrometers have been used for a discrete measurement from a small sample within a cuvette, or perhaps at a fixed location to monitor the progress of a chemical process.  However, with new camera technology and advanced image processing algorithms, there’s an influx of new spectral imaging technologies opening new applications, and in some cases displacing traditional single-point monitoring spectrometers.
During my postgraduate studies (some years ago now!), I investigated measurements of microvascular blood flow and oxygenation in skin, muscle and other tissues. These measurements were taken from single discrete locations on the surface of the skin or other tissues. Monitoring changes at these localised points provided useful information relating to blood flow dynamics during medical or pharmaceutical interventions. However, there are large heterogeneities in tissue perfusion, so the development of perfusion imaging techniques enabled better understanding of the distribution of microvascular flow and oxygenation. It’s a similar situation with spectroscopy. I’ve worked as a technical sales and applications specialist in spectroscopy for many years finding spectroscopic solutions for a variety of applications. In many situations, single point spectrometer measurements provide valuable data. However, new spectral imaging techniques are improving our understanding and bringing new insights. Hyperspectral imagers originally developed to provide spectral images from satellites and aircraft are now also being increasingly used in medical research, machine vision, food science, materials analysis and remote sensing for agriculture and minerology.
Spectral Imaging in Remote Sensing
Spectroscopy is widely used in plant science and agriculture for the general assessment of plant health or for the detection and identification of plant diseases. Many researchers use portable spectroradiometers out in the field to sample individual plant leaves in situ. In general, vegetation that is photosynthetically active absorbs red light and reflects green and near infrared light. Portable spectroradiometers measuring wide spectral ranges from 350 to 2500nm enable various spectrally derived vegetative indices to be calculated that indicate plant health or metrics such as chlorophyll or nitrogen content. However, measurements from small samples of leaves are not necessarily representative of the whole field. Â
A standard colour (RGB) aerial photograph may provide an image of the whole field with areas of discoloured leaves within a field of otherwise healthy plants, but interpretation based on a simple RGB colour photo is very limited. Hyperspectral imaging, with a camera mounted on an UAV (unmanned aerial vehicle or drone) is a much more powerful technique. Â It combines the benefits of seeing an image with the data-rich information provided by spectroscopy as it acquires an entire spectrum at each point (pixel) within the image. Multispectral imaging also provides an image but only contains data from a handful of spectral bands at each pixel. The choice between using hyperspectral or multispectral imaging depends on the job at hand. Images relating to general plant health based on vegetative indices can usually be generated based on a few spectral bands so can be addressed by multi-spectral imaging. However, applications relying on more subtle differences in spectral features require the more finely resolved hyperspectral data. So, for identification of tree species or distinguishing between more subtle differences between diseased and healthy crops , hyperspectral imaging is the more appropriate technique.
