An imaging spectrometer is an instrument used in hyperspectral imaging and imaging spectroscopy to acquire a spectrally-resolved image of an object or scene, usually to support analysis of the composition the object being imaged. The spectral data produced for a pixel is often referred to as a datacube due to the three-dimensional representation of the data. Two axes of the image correspond to vertical and horizontal distance and the third to wavelength. The principle of operation is the same as that of the simple spectrometer, but special care is taken to avoid optical aberrations for better image quality. Example imaging spectrometer types include: filtered camera, whiskbroom scanner, pushbroom scanner, integral field spectrograph (or related dimensional reformatting techniques), wedge imaging spectrometer, Fourier transform imaging spectrometer, computed tomography imaging spectrometer (CTIS), image replicating imaging spectrometer (IRIS), coded aperture snapshot spectral imager (CASSI), and image mapping spectrometer (IMS).
Background In 1704, Sir Isaac Newton demonstrated that white light could be split up into component colours. The subsequent history of spectroscopy led to precise measurements and provided the empirical foundations for atomic and molecular physics (Born & Wolf, 1999). Significant achievements in imaging spectroscopy are attributed to airborne instruments, particularly arising in the early 1980s and 1990s (Goetz et al., 1985; Vane et al., 1984). However, it was not until 1999 that the first imaging spectrometer was launched in space (the NASA Moderate-resolution Imaging Spectroradiometer, or MODIS). Terminology and definitions evolve over time. At one time, >10 spectral bands sufficed to justify the term imaging spectrometer but presently the term is seldom defined by a total minimum number of spectral bands, rather by a contiguous (or redundant) statement of spectral bands.
Principle Imaging spectrometers are used specifically for the purpose of measuring the spectral content of light and electromagnetic light. The spectral data gathered is used to give the operator insight into the sources of radiation. Prism spectrometers use a classical method of dispersing radiation by means of a prism as a refracting element. The imaging spectrometer works by imaging a radiation source onto what is called a "slit" by means of a source imager. A collimator collimates the beam that is dispersed by a refracting prism and re-imaged onto a detection system by a re-imager. Special care is taken to produce the best possible image of the source onto the slit. The purpose of the collimator and re-imaging optics are to take the best possible image of the slit. An area-array of elements fills the detection system at this stage. The source image is reimaged, every point, as a line spectrum on what is called a detector-array column. The detector array signals supply data pertaining to spectral content, in particular, spatially resolved source points inside source area. These source points are imaged onto the slit and then re-imaged onto the detector array. Simultaneously, the system provides spectral information about the source area and its line of spatially resolved points. The line is then scanned in order to build a database of information about the spectral content. In imaging spectroscopy (also hyperspectral imaging or spectral imaging) each pixel of an image acquires many bands of light intensity data from the spectrum, instead of just the three bands of the RGB color model. More precisely, it is the simultaneous acquisition of spatially coregistered images in many spectrally contiguous bands. Some spectral images contain only a few image planes of a spectral data cube, while others are better thought of as full spectra at every location in the image. For example, solar physicists use the spectroheliograph to make images of the Sun built up by scanning the slit of a spectrograph, to study the behavior of surface features on the Sun; such a spectroheliogram may have a spectral resolution of over 100,000 ( λ / Δ λ {\displaystyle \lambda /\Delta \lambda } ) and be used to measure local motion (via the Doppler shift) and even the magnetic field (via the Zeeman splitting or Hanle effect) at each location in the image plane. The multispectral images collected by the Opportunity rover, in contrast, have only four wavelength bands and hence are only a little more than 3-color images.
Unmixing Hyperspectral data is often used to determine what materials are present in a scene. Materials of interest could include roadways, vegetation, and specific targets (i.e. pollutants, hazardous materials, etc.). Trivially, each pixel of a hyperspectral image could be compared to a material database to determine the type of material making up the pixel. However, many hyperspectral imaging platforms have low resolution (>5m per pixel) causing each pixel to be a mixture of several materials. The process of unmixing one of these 'mixed' pixels is called hyperspectral image unmixing or simply hyperspectral unmixing. A solution to hyperspectral unmixing is to reverse the mixing process. Generally, two models of mixing are assumed: linear and nonlinear. Linear mixing models the ground as being flat and incident sunlight on the ground causes the materials to radiate some amount of the incident energy back to the sensor. Each pixel then, is modeled as a linear sum of all the radiated energy curves of materials making up the pixel. Therefore, each material contributes to the sensor's observation in a positive linear fashion. Additionally, a conservation of energy constraint is often observed thereby forcing the weights of the linear mixture to sum to one in addition to being positive. The model can be described mathematically as follows:
p = A ∗ x {\displaystyle p=A*x\,}
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