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he says. A small gain is used if the signal is strong, while a large gain is required for a faint signal. The precision of the detector is therefore very good, as it can adjust the gain for each pixel line and gather information even for a very faint signal.
Image of the French Frigate Shoals, an atoll consisting of a 35km crescent-shaped reef located in the Pacific Ocean about 800km northwest of Honolulu, Hawaii. The image was acquired with the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the European Space Agency’s Proba-1 satellite, designed to acquire hyperspectral images with a spatial resolution of 17m across an area of 13km
a spectral band, which are then combined into a 3D data cube and from the signal intensity at the wavelengths corresponding to water molecules, humidity levels in the scene can be determined.
Hyperspectral imaging is also used in mineralogy to provide data on the chemical composition of rocks and areas of land. Environmental disasters, such as oil spills, can also be monitored with the images showing the evolution of oil concentrations in the ocean. PRISMA will aim to generate information
on land cover, crop status, pollution quality of inland water systems, the status of coastal zones and the Mediterranean Sea, soil composition, and the carbon cycle, monitoring specifically Europe and the Mediterranean region. Sofradir’s detectors used in PRISMA have
256 lines, providing a spectral resolution at up to 256 wavebands covering visible, NIR and SWIR ranges (0.4-2.5µm). ‘The key challenge is positioning the optical components in front of the detector to partition the light into 256 different wavebands,’ says Jacques Chautemps, sales and marketing manager for space applications at Sofradir. ‘There are two ways to do this: either a prism or a grating that selects, for example, 100 wavebands of interest.’ Chautemps comments that another important feature of the detector is that the gain can be selected for each line. ‘Within a spectrum of radiation, there are both faint and intense signals reaching the detector depending on the emission or absorption of the molecule,’
Knowing when to harvest Spectral imaging from space can provide data on the vegetation cover of the land and even the optimum harvest time for a field of corn or wheat. To the same effect, monitoring the chemical composition of the soil in a field is also possible using this imaging technique. Maps of variables such as water and nutrient levels along with pest infestations allow farmers to take rapid, targeted action, which minimises cost and environmental impact while maximising crop yields. An environmental monitoring project by Carinthian Tech Research, a non-university R&D centre based in Austria, has made use of multispectral cameras from Dutch company Quest Innovations to classify different land types and calculate vegetation indices. The multispectral imaging system was mounted on a Schiebel unmanned aerial vehicle (UAV) and could simultaneously capture three visible and two NIR channels. The aim of the project was to develop a technique for classifying vegetation cover from multispectral images taken from the UAV at a height of around 100m. The images were analysed with the Normalised Difference Vegetation Index (NDVI) algorithm, commonly
‘Environmental disasters, such as oil spills, can also be monitored’
used to analyse satellite image data in assessing vegetation cover. Test flights were made in January and February 2010, with the resultant images providing an accurate assessment of vegetation cover. ‘Multispectral imaging combines spectral information with machine vision information,’ explains Richard Meester, president and CEO of Quest Innovations. ‘It could be thought of as basically 3D imaging, where the third dimension is not a height profile, but rather is spectral information. Spectroscopy provides single points of data. The spectral information is combined with the visible information of a 2D image at each pixel.’ The camera used in the environmental
Credit: ESA
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