There is a high demand for Imaging Fourier Transform Spectrometer (IFTS) hyperspectral data. Hyperspectral IFTS data is useful in a variety of fields, including atmospheric sounding (temperature, moisture, ozone), climate (greenhouse gas measurements), agriculture, and others.
Hyperspectral imaging, like other spectral imaging, collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain a spectrum for each pixel in an image, such as to find objects, identify materials, and/or detect processes.
There are two general branches of spectral imagers: push broom scanners and related whisk broom scanners, which read images over time; and snapshot hyperspectral imaging, which uses a staring array to generate an image in an instance.
The human eye sees color of visible light in mostly three bands (long wavelengths, perceived as red, medium wavelengths, perceived as green, and short wavelengths, perceived as blue). Whereas spectral imaging divides the spectrum into many more bands. This technique of dividing images into bands can be extended beyond the visible. In hyperspectral imaging, the recorded spectra have fine wavelength resolution and cover a wide range of wavelengths. Hyperspectral imaging measures contiguous spectral bands, as opposed to multispectral imaging which measures spaced spectral bands.
Hyperspectral sensors collect orders of magnitude more data than traditional multi-spectral instruments.
An advantage of hyperspectral imaging is that, because an entire spectrum is acquired at each point, an operator needs no prior knowledge of the sample, and post processing allows all available information from the dataset to be mined. Hyperspectral imaging can also take advantage of the spatial relationships among the different spectra in a neighborhood, allowing more elaborate spectral-spatial models for a more accurate segmentation and classification of the image.
Challenges of hyperspectral imaging include cost and complexity. Fast computers, sensitive detectors, and large data storage capacities are needed for analyzing hyperspectral data. Significant data storage capacity is necessary since hyperspectral cubes are large, multidimensional datasets. All of these factors greatly increase the cost of acquiring and processing hyperspectral data. Another challenge is finding ways to program hyperspectral satellites to sort through data on their own and transmit only the most important images, as both transmission and storage of that much data could prove difficult and costly. As a relatively new analytical technique, the full potential of hyperspectral imaging has not yet been realized.