The main objective of imaging spectroscopy (also known as hyperspectral imaging in the industrial and military communities) is to measure the spectral signatures and/or chemical composition of all features within the sensor's field of view. Hyperspectral data contains both spatial and spectral information from materials within a given scene. Each pixel across a sequence of continuous, narrow spectral bands, contains both spatial and spectral properties. Pixels are sampled across many narrowband images at a particular spatial location within the "spectral cube", resulting in a one-dimensional spectrum. The spectrum is a plot of wavelength versus radiance or reflectance. The spectrum can be used to identify and characterize a particular feature within the scene, based on unique spectral signatures or "fingerprints". Spectral data can be obtained using either space-based or airborne platforms, and typically involves scanning many narrowband images simultaneously, while using some type of dispersion grating to produce the spectrum.
Hyperspectral imaging in the emissive region of the electromagnetic spectrum typically involves the portion of the electromagnetic spectrum associated with primarily vibrational motion of molecules, and to some degree rotational and vibrational-rotational modes. These modes of molecular vibrations occur in the mid-infrared (3 to 5 microns) and the longwave-infrared (8-14 microns). The mid-IR and longwave-IR are sometimes referred to as the "fingerprint" region of the electromagnetic spectrum, since many effluents and gases have distinctive absorption features used in their identification. The analysis of effluents and gases between 3 to 5 microns can be problematic since both reflective and emissive properties are involved. The region of the EM spectrum associated with electronic transitions of molecules is the visible to near-IR, and also the shortwave-IR (SWIR). Analysis of hyperspectral data across the visible, near-IR and SWIR portion of the spectrum deal with the reflective nature of solids and liquid materials.
The reflective region of the spectrum ranges from 0.38 to about 3 microns, where the shorter wavelength is limited by Earth's atmospheric cutoff in the near-UV. The emissive region ranges from 7 to 15 microns when applied to hyperspectral imaging. LWIR sensors are expensive to build, so the are very few hyperspectral sensors that operated over the wavelength regime. The wavelength range from 3 to 5 microns containes a mixed contribution from both reflective and emissive radiation, making this region of the spectrum difficult to analyze. The mixed region from 3 to 5 microns (MWIR) is very useful for identifying specific gases. The gases have many unique absorption features in their spectral signatures, making identification possible. The LWIR region (8 to 14.5 microns) is also used to identify various gases.
Note the complex mixture of both reflective and emissive properties between about 3 and 7 microns (MWIR region), making this region of the EM spectrum difficult to work in. The Earth has a peak emission near 10 microns corresponding to a temperature of approximately 289 K (from Wien's Displacement Law). The Earth's peak reflectance is centered near 0.5 microns (for the Sun's effective temperature of 5780 K). It is interesting to note that this wavelength of peak emission is optimal for the visual acuity of humans.
One advantage of working in the LWIR is that there are no problems associated with solar illumination as encountered in the VNIR/SWIR portion of the spectrum, where it is the reflective properties of the target that dominate. Imaging spectrometers operating the the LWIR can also be used at night. In both the MWIR and LWIR, it is the emissive properties of materials that dominate their nature. Different materials within a given scene can be identified and characterized based on their unique emissivity, a measure of how efficiently a particular material radiates energy in comparison with a blackbody (i.e. a perfect emitter and absorber) at the same temperature. The emissivity of a material depends on the wavelength and the molecular properties of the material, and it is the unique emissive signature of the object that can be used to identify it when working in the Mid-IR or Longwave-IR portion of the spectrum.
The difference between multispectral and hyperspectral imaging is illustrated in the diagram shown below. Broadband sensors typically produce panchromatic images with very wide bandwidths, typically 400 nanometers. WorldView-1, for example, produced broadband (panchromatic) images with a high spatial resolution of 50 centimeters. The bandwidth of WorldView-1's panchromatic images is 500 nanometers, ranging from 400 to 900 nanometers. Multispectral imaging involves taking imagery over several discrete spectral bands of moderate bandwidth. Most multispectral imagers have four basic spectral bands; blue, green, red, and near-IR. Some multispectral imaging satellites, such as Landsat 7 have additional spectral bands in the infrared region of the spectrum. Hyperspectral imaging systems can obtain imagery over hundreds of narrow, continuous spectral bands with typical bandwidths of 10 nanometers or less. For example, the AVIRIS airborne hyperspectral imaging sensor obtains spectral data over 224 continuous channels, each with a bandwidth of 10 nm over a spectral range from 400 to 2500 nanometers. An example of an operational space-based hyperspectral imaging platform, is the Air Force Research Lab's TacSat-3/ARTEMIS sensor, which has 400 continuous spectral channels, each with a bandwidth of 5 nm. Ultraspectral sensors represent the future of hyperspectral imaging technology. These sensors are defined to have 1000s of spectral channels, each with a bandwidth narrower than those of hyperspectral sensors (<0.1 nm) (Kudenov, et al., 2015. Micro- and Nanotechnology Sensors, Systems, and Applications VII, Proc. SPIE 9467).
Panchromatic imagery is good for detecting various objects, materials and activities within a given area. Multispectral imagery can provide information on broad classes of scene features, such as the presence of healthy vegetation, bodies of water - i.e. multispectral data allows an analyst to separate scene features into generic classes, as well as into features that have similar spectral properties. Hyperspectral imaging allows much finer sampling of the spectrum of scene features. It can be used to identify, and in some cases characterize scene features based on their unique spectral signatures (absorption bands or emissive features). Ultraspectral (in addition to some advanced hyperspectral sensors with especially narrow spectral bandwidths) sensors will allow a quantitative assessment of scene materials (solids, liquids and gases). For example, the abundance of different gases or effluents could be determined based on the width and strength of absorption features in a given spectrum.
The spectra shown below illustrates an example of an observed scene feature, in this case, the mineral Alunite - a sulfate mineral found in volcanic rocks, typically formed in acid-sulfate hydrothermal-vein systems (e.g. Yellowstone National Park). The three spectral clearly show the advantage of hyperspectral imaging over multispectral sensors (MODIS and TM). The laboratory collected spectrum with a spectral resolution typical of hyperspectral imaging systems has a much finer sampling than the two spectra collected with multispectral sensors. The high-resolution spectral shows many unique features (absorption bands) that are not seen in the multispectral data (top two spectra). The multispectral sensors would not be able to identify the mineral Alunite based on its distinctive absorption doublet near 1.45 microns.
Sub-pixel detection allows one to quantitatively determine fractional abundances of materials within a single pixel using advanced mathematical techniques (described later in the presentation). This allows analysts to detect particular materials that occupy less than one pixel (illustrated by the yellow color), provided there is high spectral contrast between the target material and its background. A significant advantage of hyperspectral imaging over most other methods of remote sensing is that a sensor does not have to resolve a given target to obtain information on the target itself.
The diagram below shows the basic scenarios leading to the formation of absorption features, emission features and no features in spectra. A warm gas between the sensor and a cold background will result in a spectrum with various emission lines. The reverse is true when a gas plume/cloud is located between the sensor and a warm background - i.e. absorption features are produced. It is these unqiue emission and absorption lines that are used to identify the particular gas. If there is no temperature difference between the gas plume/cloud and background then no emission or aborption features will be seen in the spectrum, making identification of the gas not possible.
There exist many applications of hyperspectral imaging over different regions of the electromagnetic spectrum, ranging from the VNIR to LWIR. For example, the military typically designs various camouflage to mimic the spectral signature of vegetation. This allows the material to blend in with background vegetation when viewed using spectral sensors having near-IR bands. Both background vegetation and the material show the unique "red edge" feature of vegetation. However, hyperspectral sensors using both Near-IR and SWIR spectral bands can be used to discriminate camouflage material from background vegetation, since there are significant differences in spectral signatures between camouflage and vegetation in the Shortwave-IR (SWIR), due to differences in the moisture content of background vegetation, for example.
A typical workflow for the analysis of hyperspectral data is summarized in the diagram below. Typically, one corrects raw hyperspectral "data cubes" for atmospheric effects. This converts radiance to reflectance so that observed spectra can be compared to library reference spectra. Atmospheric correction is the most critical processing step in hyperspectral data analysis. A bad atmospheric correction will result in false-positives when the data is analysed using various techniques. One of the first steps to analysing spectral cubes is to separate noisy spectral bands from the data, and to eliminate highly redundant spectral bands typical of hyperspectral data. This is done using what is known as a Minimum Noise Fraction transform, which is essentially two cascaded Principle Component transforms, the second transform being performed on noise whitened data. The MNF transform essentially reduces the dimensionality of the hyperspectral data, facilitating faster processing by computers. Further information about PC and MNF transforms are available in many books and papers covering the processing of hypespectral imaging. Once the data has been reduced to the most important/relevant spectral bands, one typically derives spectral "endmembers" from the data. An endmember is a term used to describe a pure spectral signature of a particular material. These endmembers can be derived from the data itself, or from spectral libraries and field collected spectra. After the endmembers are collected from the data itself or library spectra, they are used by various spectral mapping methods. The main goal of these various spectral mapping methods is to produce a final product, such as a scene classification or thematic map, a material identification map, or target detection map.
One of the most commonly used spectral mapping methods is Spectral Angle Mapper or SAM. This methods simply treats each spectrum as a vector in an n-dimensional scatter plot (or n-D space). The mathematical technique computes an angle between the reference and observed spectrum. The smaller the angular separation, the closer the match between the observed and reference spectra.
More advanced spectral mapping methods include Matched Filters and Adaptive Coherence Estimators. Due to time constraints, the specific mathematics will not be discussed here, but for reference the actual equations used by these advanced methods are shown for reference. Essentially, matched filters and similar techniques try to maximize the response of the target spectrum, while supressing background clutter. The Adaptive Coherence Estimator or ACE models the background clutter using the data's statistics (covariance matrix). ACE is commonly used as a target finding technique since one does not have to have knowledge of all the endmembers within a given scene, and because the method does not depend on the relative scaling of input spectra. Some hyperspectral analysis tools improve upon the conventional Matched Filter by incorporating an "infeasibility parameter" that describes how likely a "false positive" is (e.g. ENVI's Mixture Tuned Matched Filter). These advanced matched filters essentially combine the benefits of both conventional matched filter techniques and linear mixture theory. This makes the Mixture Tuned Matched Filter especially useful for sub-pixel analysis of scene materials.
Linear Unmixing (appplied to areal mixtures) involves the solution of a set of "n" linear equations for each pixel, where n is the number of spectral bands. The result of the solution is a set of fractional abundances for each material within the single pixel. The ability to perform linear unmixing on hyperspectral data allows analysts to identify materials or objects within a given scene, that are not necessarily resolved in the image. This is an example of what is known as "Non-literal" analysis, in contrast to literal analysis where objects are identified by eye. For intimate mixtures of granular materials, nonlinear unmixing techniques are applied.
The high spectral resolution of hyperspectral sensors allows the clear identification of the "red edge" feature of healthy vegetation. This feature is the result of the high reflectivity in the near-IR and absorption in the red spectral bands. Vegetation that is stressed will show higher reflectivity in the Shortwave-IR portion of the spectrum. A complete understanding of the high-resolution spectral signature of vegetation involves the particular state of the cell structure, water content, biochemicals, and pigments within the vegetation. Healthy vegetation will absorb in both the blue and red bands, giving rise to what is called the "green bump of healthy vegetation". As vegetation is stressed, or as the vegetation's chlorophyll content changes, the "green bump" feature will change, along with the reflectivity in the near-IR and shortware-IR portions of the spectrum. When viewed using the standard false-color composite (Near-IR/Red/Green composite), heathy vegetation will show up as deep red). A quantitative measurement of the health and density of vegetation is carried out using the Normalized Difference Vegetation Index or NDVI, a contrast ratio using a red and near-IR spectral band, (NIR-Red)/(NIR+Red). Index values can range between -1.0 and 1.0, but vegetation has values that typically range between 0.1 and 0.7.
The images below show how hyperspectral imaging (in this case data obtained from the Hyperion spaced based sensor) can be used to image burn scars and hot spots (seen as orange and bright orange spots on the right image) through smoke resulting from wildfires. The smoke is more transparent in the SWIR bands than in the VNIR bands. Using a contrast ratio of two different SWIR bands, a Burn Index (BI) can be created to measure the severity of burn scars.
The spectral maps below show an example of mineral mapping, one of the major applications of hyperspectral imaging where high spectral resolution is necessary to identify specific minerals from their unique absorption features produced by the interaction of radiation with the mineral's unqiue crystalline structure. In this example, a Matched filter was used along with a USGS reference spectrum of the water-alteration mineral Kaolinite, to detect is location at Cuprite, Nevada. In the MF detection map, the white areas indicate the presence of Kaolinite. The Minimum Noise Transform (shown in lower left image) reveals the diversity of minerals at the Cuprite, Nevada calibration test site. The top left pane shows the difference between the USGS reference spectrum (blue line) and the actual AVIRIS spectrum (red line). The fit to the specific absorption doublet feature at slightly less than 2.2 microns indicates the identification of the mineral Kaolinite. The SWIR portion of the spectrum between 2.0 and 2.5 microns is most commonly used to map minerals.
The following slide shows Matched Filter detections of three different alteration minerals at the Cuprite, Nevada site. Kaolinite, Alunite, and Buddingtonite are shown as different color overlays on top of a single baseline SWIR band.
Hyperspectral imaging is especially useful for assessing environmental disasters, such as the 2010 Gulf Oil Spill. The location of oil slicks floating on the surface of ocean water can be identified using several unique absorption bands due to the C-H bond of the hydrocarbon. Small amounts of oil are sensitive to the 2.3-micron absorption feature, which is caused by different rotational modes of the hydrocarbon molecule. Thicker amounts of oil are sensitive to the 1.73-micron absorption feature, which is the result of the hydrocarbon molecule's strech mode. In contrast to multispectral imaging, which can locate oil slicks by their distinctive color on ocean water, hyperspectral imaging allows a quantitative assessment of the amount of oil present.
There are also many military applications of hyperspectral imaging. The high spectral resolution of hyperspectral sensors allows one to discriminate not only camouflage from background clutter, but different types of camouflage. Note the common spectral feature of two types of camouflage. They all "mimic" the red edge of vegetation, so they would all appear to blend in with background vegetation if they were imaged using conventional NIR/Red/Green multispectral imaging systems. However, hyperspectral imaging systems with expanded spectral coverage in addition to higher spectral resolution can differentiate the different types of camouflage, especially when examined in the SWIR portion of the spectrum. The SWIR bands also allow the discrimination between the two types of camouflage and the background vegetation.
In conclusion, every feature within a given scene has unique spectral properties due to its molecular structure, and the way that molecular structure interacts with radiation to cause reflective or emissive signatures. The LWIR and to some extent the MWIR are known as the "finger print" region of the spectrum for identifying gases and effluents. Spatial resolution is not as important as spectral resolution in hyperspectral imaging applications, since sub-pixel analysis using various advanced mathematical methods is possible. Derivative spectroscopy is a hot topic of research within the hyperspectral imaging community. It is used to enhance/amplify very minor details in spectral signatures. The future of hyperspectral imaging technology is leaning towards the use of active hyperspectral imaging techniques, where the imaging system provides its own source of controlled illumination. This technique promises to reduce or eliminate problems associated with solar illumination artifacts and shadows encountered with today's conventional hyperspectral imaging systems.
Future space-based optical interferometers equipped with imaging spectrometers will be able to obtain integrated spectra of the full disk of earth-like exoplanets to search for biosignatures in their atmospheres. Potential biosignature gases include
Water 2.7 microns, 6.3 microns, 19.51 microns Nitrous Oxide 3.8 microns, 4.5 microns, 7.78 microns, 17 microns Methane 3.3 microns, 7.7 microns Ozone 9.65 microns Oxygen 0.69 microns, 0.76 microns, 1.26 microns Carbon Dioxide 2.7 microns, 4.3 microns, 15 microns Carbon Monoxide 4.7 microns Nitric Acid 11.5 microns Chlorophyll a 6.76 microns Other potential biosignatures include Ammonia, Sulfur Dioxide, H2S, CH3OH, CH3Cl, and DMS
Solid Signatures on Rocky Planets H2O Ice 1.25, 1.5, 2.0 microns Silicates 1.0, 2.0 microns (broad) Ferric Oxides 1.0 microns Carbonates 2.35, 2.5 microns Hydrated Silicates: 3.0-3.5 microns (broad)
Pigments in Earth-sized planets orbiting stars somewhat brighter than the Sun could absorb blue (450 nm) and reflect yellow, orange, red, or a combination of these colors.For stars cooler than the Sun (M Type), evolution might favor photosynthetic pigments to pick up the full range of visible and IR light. With little light reflected, plants might look dark to human eyes. The red edge spectral position could be shifted for other Earth-like planets with a different parent star.
Photosynthesis on Earth produces the most detectable signs of life at the global scale. The presence of oxygen or ozone in an atmosphere simultaneously with reduced gases like methane is considered a robust biosignature (Des Marais et al., 2002). A challenging, complementary observation to atmospheric oxygen would be detection of the vegetation red edge - the strong contrast in red absorbance and near-infrared reflectance of plant leaves due to green chlorophyll. Although the reason for the placement of the Earth’s rededge at 0.7 microns is still not fully explained, scientists have proposed it is due to the function of chlorophyll a (Björn et al. 2009).
Terrestrial biosignatures resulting from biological species include a disequilibrium in atmospheric gas species, the red-edge of plant life due to the enhanced reflectivity in the near-IR and strong absorption in the red, and biosignatures that vary with time, such as seasonal variations in atmospheric composition and/or surface albedo.
Any small amount of molecular oxygen in an earth-like planet's atmosphere produced by photolysis of water vapor is consumed thrugh oxidation of surface rocks and volcanic gases. Thus, if oxygen and liquid water are simultaneously observed in a spectrum, there must be some additional source producing the oxygen. The most likely source would be oxygenic photosynthesis. If ozone and liquid water are seen in a spectrum, it would be a very strong biosignature. The formation of ozone (O3) requires the presence of oxygen in the planet's atmosphere, since UV radiation dissociates molecular oxygen, which then recombines to form ozone. Ozone has a spectral signature in the infrared part of the spectrum, making it easier to detect than oxygen (which is detected at visible wavelengths). If both oxygen and methane are detected together, it is a strong indication that photosynthesis is occurring. Also, if imaging spectroscopy detects a seasonal trend (variation) of methane abundances, it is an indication of life because methane levels will eventually begin to decrease due to dissociation from stellar radiation. Methyl chloride might be an indicator of burning plant life due to fires. It is also due to an interaction between sunlight and ocean plankton and chlorine in seawater on Earth. However, oxidation acts as a sink and its signature may be too weak to detect. Nitrous oxide is released as vegetation decays on Earth. Nitrogen is released in the form of nitrous oxide. Since abiotic sources of this gas (lightning, etc.) are negligible, it could be used as a possible biosignature.
The following diagrams show simulated (synthetic) spectra of earth-like planets. The spectra cover the spectral range from 0.5 to 20 microns. Many of the discussed biosignatures are visible in the spectra. The bottom spectra are for an Earth-sized planet (1 g) around the Sun (black), AD Leo (red), M0 dwarf star (green), M5 dwarf star (blue), and an M7 dwarf (magenta). The following spectra are courtesy of H. Rauer et al.: Potential Biosignatures in super-Earth Atmospheres. Astronomy & Astrophysics, February 16, 2011.
The three spectra shown below for Venus, Earth, and Mars illustrate the effect of life on Earth. All three terrestrial planets shown strong absorption in their atmospheres due to carbon dioxide. Only the Earth's atmosphere shows two biosignatures due to life, water and ozone.
The "red edge" feature of vegetation is another possible biosignature. However, the red edge may shift due to different types of plant life and the spectral class of the host star. Photosynthesis from plant life produces molecular oxygen. The dissociation of H2O by UV photons, which produces O2, appears to be an inorganic process. On Earth, oxygen is stored in the atmosphere, and part of it is destroyed by oxidation of anoxidized rocks, freshly delivered by tectonic activity. However, the oxygen content of Earth's atmosphere is significant (about 20%) because of the dominance of oxygen production from biogenic sources. The ratio of O3/O2 is an indicator of the degree of evolution of biological activities on an earth-like planet. Ozone is produced by the photolysis of oxygen. Ozone is a good tracer of oxygen in the atmosphere of an Earth-like world. The spectroscopic detection of molecular oxygen and a reduced gas (methane or nitrous oxide) provides very strong evidence for the presence of life on an Earth-like planet.
During the 1990 flyby of Earth of the NASA Galileo spacecraft, Carl Sagan (1993) carried out a controled experiment using the NIMS instrument to look for biosignatures on Earth. The spectrometer found abundant molecular oxygen in atmosphere, a sharp absorption edge in the red part of the visible spectrum due to vegetation, and atmospheric methane in extreme thermodynamic disequilibrium. All of these biosignatures are highly suggestive of life on Earth. The spacecraft also found evidence of intelligent life from the presence of narrowband, pulsed, amplitude-modulated radio transmissions.
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