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Hyperspectral imaging
Hyperspectral imaging
Hyperspectral imaging
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Two-dimensional projection of a hyperspectral cube.

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum.[1] The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.[2][3] There are three general types of spectral imagers. There are push broom scanners and the related whisk broom scanners (spatial scanning), which read images over time, band sequential scanners (spectral scanning), which acquire images of an area at different wavelengths, and snapshot hyperspectral imagers, which uses a staring array to generate an image in an instant.

Whereas 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), 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 continuous spectral bands, as opposed to multiband imaging which measures spaced spectral bands.[4]

Engineers build hyperspectral sensors and processing systems for applications in astronomy, agriculture, molecular biology, biomedical imaging, geosciences, physics, and surveillance. Hyperspectral sensors look at objects using a vast portion of the electromagnetic spectrum. Certain objects leave unique "fingerprints" in the electromagnetic spectrum. Known as spectral signatures, these "fingerprints" enable identification of the materials that make up a scanned object. For example, a spectral signature for oil helps geologists find new oil fields.[5]

Sensors

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Figuratively speaking, hyperspectral sensors collect information as a set of "images." Each image represents a narrow wavelength range of the electromagnetic spectrum, also known as a spectral band. These "images" are combined to form a three-dimensional (x, y, λ) hyperspectral data cube for processing and analysis, where x and y represent two spatial dimensions of the scene, and λ represents the spectral dimension (comprising a range of wavelengths).[6]

Technically speaking, there are four ways for sensors to sample the hyperspectral cube: spatial scanning, spectral scanning, snapshot imaging,[5][7] and spatio-spectral scanning.[8]

Hyperspectral cubes are generated from airborne sensors like NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), or from satellites like NASA's EO-1 with its hyperspectral instrument Hyperion.[9][10] However, for many development and validation studies, handheld sensors are used.[11]

The precision of these sensors is typically measured in spectral resolution, which is the width of each band of the spectrum that is captured. If the scanner detects a large number of fairly narrow frequency bands, it is possible to identify objects even if they are only captured in a handful of pixels. However, spatial resolution is a factor in addition to spectral resolution. If the pixels are too large, then multiple objects are captured in the same pixel and become difficult to identify. If the pixels are too small, then the intensity captured by each sensor cell is low, and the decreased signal-to-noise ratio reduces the reliability of measured features.

The acquisition and processing of hyperspectral images is also referred to as imaging spectroscopy or, with reference to the hyperspectral cube, as 3D spectroscopy.

Scanning techniques

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Photos illustrating individual sensor outputs for the four hyperspectral imaging techniques. From left to right: Slit spectrum; monochromatic spatial map; 'perspective projection' of hyperspectral cube; wavelength-coded spatial map.

There are four basic techniques for acquiring the three-dimensional (x, y, λ) dataset of a hyperspectral cube. The choice of technique depends on the specific application, seeing that each technique has context-dependent advantages and disadvantages.

Spatial scanning

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Acquisition techniques for hyperspectral imaging, visualized as sections of the hyperspectral datacube with its two spatial dimensions (x, y) and one spectral dimension (λ).

In spatial scanning, each two-dimensional (2D) sensor output represents a full slit spectrum (x, λ). Hyperspectral imaging (HSI) devices for spatial scanning obtain slit spectra by projecting a strip of the scene onto a slit and dispersing the slit image with a prism or a grating. These systems have the drawback of having the image analyzed per lines (with a push broom scanner) and also having some mechanical parts integrated into the optical train. With these line-scan cameras, the spatial dimension is collected through platform movement or scanning. This requires stabilized mounts or accurate pointing information to 'reconstruct' the image. Nonetheless, line-scan systems are particularly common in remote sensing, where it is sensible to use mobile platforms. Line-scan systems are also used to scan materials moving by on a conveyor belt. A special case of line scanning is point scanning (with a whisk broom scanner), where a point-like aperture is used instead of a slit, and the sensor is essentially one-dimensional instead of 2D.[7][12]

Spectral scanning

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In spectral scanning, each 2D sensor output represents a monochromatic (i.e. single wavelength), spatial (x, y)-map of the scene. HSI devices for spectral scanning are typically based on optical band-pass filters (either tunable or fixed). The scene is spectrally scanned by exchanging one filter after another while the platform remains stationary. In such "staring", wavelength scanning systems, spectral smearing can occur if there is movement within the scene, invalidating spectral correlation/detection. Nonetheless, there is the advantage of being able to pick and choose spectral bands, and having a direct representation of the two spatial dimensions of the scene.[6][7][12] If the imaging system is used on a moving platform, such as an airplane, acquired images at different wavelengths corresponds to different areas of the scene. The spatial features on each of the images may be used to realign the pixels.

Non-scanning

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Main article: Snapshot hyperspectral imaging

In non-scanning, a single 2D sensor output contains all spatial (x, y) and spectral (λ) data. HSI devices for non-scanning yield the full datacube at once, without any scanning. Figuratively speaking, a single snapshot represents a perspective projection of the datacube, from which its three-dimensional structure can be reconstructed.[7][13] The most prominent benefits of these snapshot hyperspectral imaging systems are the snapshot advantage (higher light throughput) and shorter acquisition time. A number of systems have been designed, including computed tomographic imaging spectrometry (CTIS), fiber-reformatting imaging spectrometry (FRIS), integral field spectroscopy with lenslet arrays (IFS-L), multi-aperture integral field spectrometer (Hyperpixel Array), integral field spectroscopy with image slicing mirrors (IFS-S), image-replicating imaging spectrometry (IRIS), filter stack spectral decomposition (FSSD), coded aperture snapshot spectral imaging (CASSI), image mapping spectrometry (IMS), and multispectral Sagnac interferometry (MSI).[14] However, computational effort and manufacturing costs are high. In an effort to reduce the computational demands and potentially the high cost of non-scanning hyperspectral instrumentation, prototype devices based on Multivariate Optical Computing have been demonstrated. These devices have been based on the Multivariate Optical Element[15][16] spectral calculation engine or the Spatial Light Modulator[17] spectral calculation engine. In these platforms, chemical information is calculated in the optical domain prior to imaging such that the chemical image relies on conventional camera systems with no further computing. As a disadvantage of these systems, no spectral information is ever acquired, i.e. only the chemical information, such that post processing or reanalysis is not possible.

Spatiospectral scanning

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Main article: Spatiospectral scanning

In spatiospectral scanning, each 2D sensor output represents a wavelength-coded ("rainbow-colored", λ = λ(y)), spatial (x, y)-map of the scene. A prototype for this technique, introduced in 2014, consists of a camera at some non-zero distance behind a basic slit spectroscope (slit + dispersive element).[8][18] Advanced spatiospectral scanning systems can be obtained by placing a dispersive element before a spatial scanning system. Scanning can be achieved by moving the whole system relative to the scene, by moving the camera alone, or by moving the slit alone. Spatiospectral scanning unites some advantages of spatial and spectral scanning, thereby alleviating some of their disadvantages.[8]

Distinguishing hyperspectral from multispectral imaging

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Multispectral and hyperspectral differences.

Hyperspectral imaging is part of a class of techniques commonly referred to as spectral imaging or spectral analysis. The term "hyperspectral imaging" derives from the development of NASA's Airborne Imaging Spectrometer (AIS) and AVIRIS in the mid-1980s. Although NASA prefers the earlier term "imaging spectroscopy" over "hyperspectral imaging," use of the latter term has become more prevalent in scientific and non-scientific language. In a peer reviewed letter, experts recommend using the terms "imaging spectroscopy" or "spectral imaging" and avoiding exaggerated prefixes such as "hyper-," "super-" and "ultra-," to prevent misnomers in discussion.[19]

Hyperspectral imaging is related to multispectral imaging. The distinction between hyper- and multi-band is sometimes based incorrectly on an arbitrary "number of bands" or on the type of measurement. Hyperspectral imaging (HSI) uses continuous and contiguous ranges of wavelengths (e.g. 400 - 1100 nm in steps of 1 nm) whilst multiband imaging (MSI) uses a subset of targeted wavelengths at chosen locations (e.g. 400 - 1100 nm in steps of 20 nm).[20]

Multiband imaging deals with several images at discrete and somewhat narrow bands. Being "discrete and somewhat narrow" is what distinguishes multispectral imaging in the visible wavelength from color photography. A multispectral sensor may have many bands covering the spectrum from the visible to the longwave infrared. Multispectral images do not produce the "spectrum" of an object. Landsat is a prominent practical example of multispectral imaging.

Hyperspectral deals with imaging narrow spectral bands over a continuous spectral range, producing the spectra of all pixels in the scene. A sensor with only 20 bands can also be hyperspectral when it covers the range from 500 to 700 nm with 20 bands each 10 nm wide, while a sensor with 20 discrete bands covering the visible, near, short wave, medium wave and long wave infrared would be considered multispectral.

Ultraspectral could be reserved for interferometer type imaging sensors with a very fine spectral resolution. These sensors often have (but not necessarily) a low spatial resolution of several pixels only, a restriction imposed by the high data rate.

Applications

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See also: Multispectral imaging § Applications

Hyperspectral remote sensing is used in a wide array of applications. Although originally developed for mining and geology (the ability of hyperspectral imaging to identify various minerals makes it ideal for the mining and oil industries, where it can be used to look for ore and oil),[11][21] it has now spread into fields as widespread as ecology and surveillance, as well as historical manuscript research, such as the imaging of the Archimedes Palimpsest. This technology is continually becoming more available to the public. Organizations such as NASA and the USGS have catalogues of various minerals and their spectral signatures, and have posted them online to make them readily available for researchers. On a smaller scale, NIR hyperspectral imaging can be used to rapidly monitor the application of pesticides to individual seeds for quality control of the optimum dose and homogeneous coverage.

Agriculture

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Hyperspectral camera embedded on OnyxStar HYDRA-12 UAV from AltiGator.

Although the cost of acquiring hyperspectral images is typically high for specific crops and in specific climates, hyperspectral remote sensing use is increasing for monitoring the development and health of crops. In Australia, work is under way to use imaging spectrometers to detect grape variety and develop an early warning system for disease outbreaks.[22] Furthermore, work is under way to use hyperspectral data to detect the chemical composition of plants,[23] which can be used to detect the nutrient and water status of wheat in irrigated systems.[24] On a smaller scale, NIR hyperspectral imaging can be used to rapidly monitor the application of pesticides to individual seeds for quality control of the optimum dose and homogeneous coverage.[25]

Another application in agriculture is the detection of animal proteins in compound feeds to avoid bovine spongiform encephalopathy (BSE), also known as mad-cow disease. Different studies have been done to propose alternative tools to the reference method of detection, (classical microscopy). One of the first alternatives is near infrared microscopy (NIR), which combines the advantages of microscopy and NIR. In 2004, the first study relating this problem with hyperspectral imaging was published.[26] Hyperspectral libraries that are representative of the diversity of ingredients usually present in the preparation of compound feeds were constructed. These libraries can be used together with chemometric tools to investigate the limit of detection, specificity and reproducibility of the NIR hyperspectral imaging method for the detection and quantification of animal ingredients in feed.

HSI cameras can also be used to detect stress from heavy metals in plants and become an earlier and faster alternative to post-harvest wet chemical methods.[27][28]

Zoology

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Hyperspectral imaging is also used in zoology; it is used to investigate the spatial distribution of coloration and its extension into the near-infrared and SWIR range of the spectrum.[29] Some animals for example, such as some tropical frogs and certain leaf-sitting insects are highly reflective in the near-infrared.[29][30]

Waste sorting and recycling

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Hyperspectral imaging can provide information about the chemical constituents of materials which makes it useful for waste sorting and recycling.[31] It has been applied to distinguish between substances with different fabrics and to identify natural, animal and synthetic fibers.[32] HSI cameras can be integrated with machine vision systems and, via simplifying platforms, allow end-customers to create new waste sorting applications and other sorting/identification applications.[33] A system of machine learning and hyperspectral camera can distinguish between 12 different types of plastics such as PET and PP for automated separation of waste of, as of 2020, highly unstandardized[34][additional citation(s) needed] plastics products and packaging.[35][36]

Eye care

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Researchers at the Université de Montréal are working with Photon etc. and Optina Diagnostics[37] to test the use of hyperspectral photography in the diagnosis of retinopathy and macular edema before damage to the eye occurs. The metabolic hyperspectral camera will detect a drop in oxygen consumption in the retina, which indicates potential disease. An ophthalmologist will then be able to treat the retina with injections to prevent any potential damage.[38]

Food processing

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A line scan push-broom system was used to scan the cheeses and images were acquired using a Hg-Cd-Te array (386x288) equipped linescan camera with halogen light as a radiation source.

In the food processing industry, hyperspectral imaging, combined with intelligent software, enables digital sorters (also called optical sorters) to identify and remove defects and foreign material (FM) that are invisible to traditional camera and laser sorters.[39][40] By improving the accuracy of defect and FM removal, the food processor's objective is to enhance product quality and increase yields.

Adopting hyperspectral imaging on digital sorters achieves non-destructive, 100 percent inspection in-line at full production volumes. The sorter's software compares the hyperspectral images collected to user-defined accept/reject thresholds, and the ejection system automatically removes defects and foreign material.

Hyperspectral image of "sugar end" potato strips shows invisible defects.

The recent commercial adoption of hyperspectral sensor-based food sorters is most advanced in the nut industry where installed systems maximize the removal of stones, shells and other foreign material (FM) and extraneous vegetable matter (EVM) from walnuts, pecans, almonds, pistachios, peanuts and other nuts. Here, improved product quality, low false reject rates and the ability to handle high incoming defect loads often justify the cost of the technology.

Commercial adoption of hyperspectral sorters is also advancing at a fast pace in the potato processing industry where the technology promises to solve a number of outstanding product quality problems. Work is under way to use hyperspectral imaging to detect "sugar ends,"[41] "hollow heart"[42] and "common scab,"[43] conditions that plague potato processors.

Mineralogy

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A set of stones is scanned with a Specim LWIR-C imager in the thermal infrared range from 7.7 μm to 12.4 μm. The quartz and feldspar spectra are clearly recognizable.[44]

Geological samples, such as drill cores, can be rapidly mapped for nearly all minerals of commercial interest with hyperspectral imaging. Fusion of SWIR and LWIR spectral imaging is standard for the detection of minerals in the feldspar, silica, calcite, garnet, and olivine groups, as these minerals have their most distinctive and strongest spectral signature in the LWIR regions.[44]

Hyperspectral remote sensing of minerals is well developed. Many minerals can be identified from airborne images, and their relation to the presence of valuable minerals, such as gold and diamonds, is well understood. Currently, progress is towards understanding the relationship between oil and gas leakages from pipelines and natural wells, and their effects on the vegetation and the spectral signatures. Recent work includes the PhD dissertations of Werff[45] and Noomen.[46]

Surveillance

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Hyperspectral thermal infrared emission measurement, an outdoor scan in winter conditions, ambient temperature -15°C—relative radiance spectra from various targets in the image are shown with arrows. The infrared spectra of the different objects such as the watch glass have clearly distinctive characteristics. The contrast level indicates the temperature of the object. This image was produced with a Specim LWIR hyperspectral imager.[44]

Hyperspectral surveillance is the implementation of hyperspectral scanning technology for surveillance purposes. Hyperspectral imaging is particularly useful in military surveillance because of countermeasures that military entities now take to avoid airborne surveillance. The idea that drives hyperspectral surveillance is that hyperspectral scanning draws information from such a large portion of the light spectrum that any given object should have a unique spectral signature in at least a few of the many bands that are scanned. Hyperspectral imaging has also shown potential to be used in facial recognition purposes. Facial recognition algorithms using hyperspectral imaging have been shown to perform better than algorithms using traditional imaging.[47]

Traditionally, commercially available thermal infrared hyperspectral imaging systems have needed liquid nitrogen or helium cooling, which has made them impractical for most surveillance applications. In 2010, Specim introduced a thermal infrared hyperspectral camera that can be used for outdoor surveillance and UAV applications without an external light source such as the sun or the moon.[48][49]

Astronomy

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In astronomy, hyperspectral imaging is used to determine a spatially resolved spectral image. Since a spectrum is an important diagnostic, having a spectrum for each pixel allows more science cases to be addressed. In astronomy, this technique is commonly referred to as integral field spectroscopy, and examples of this technique include FLAMES[50] and SINFONI[51] on the Very Large Telescope. The Advanced CCD Imaging Spectrometer on the Chandra X-ray Observatory uses this technique.

Chemical imaging

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Main article: Chemical imaging
Remote chemical imaging of a simultaneous release of SF6 and NH3 at 1.5 km using the Telops Hyper-Cam imaging spectrometer.[52]

Soldiers can be exposed to a wide variety of chemical hazards. These threats are mostly invisible but detectable by hyperspectral imaging technology. The Telops Hyper-Cam, introduced in 2005, has demonstrated this at distances up to 5 km.[53]

Environment

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Top panel: Contour map of the time-averaged spectral radiance at 2078 cm−1 corresponding to a CO2 emission line. Bottom panel: Contour map of the spectral radiance at 2580 cm−1 corresponding to continuum emission from particulates in the plume. The translucent gray rectangle indicates the position of the stack. The horizontal line at row 12 between columns 64-128 indicate the pixels used to estimate the background spectrum. Measurements made with the Telops Hyper-Cam.[54]

Most countries require continuous monitoring of emissions produced by coal and oil-fired power plants, municipal and hazardous waste incinerators, cement plants, as well as many other types of industrial sources. This monitoring is usually performed using extractive sampling systems coupled with infrared spectroscopy techniques. Some recent standoff measurements performed allowed the evaluation of the air quality but not many remote independent methods allow for low uncertainty measurements.

Civil engineering

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Recent research indicates that hyperspectral imaging may be useful to detect the development of cracks in pavements[55] which are hard to detect from images taken with visible spectrum cameras.[55]

Biomedical imaging

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Hyperspectral imaging has also been used to detect cancer, identify nerves and analyze bruises.[56]

Advantages and disadvantages

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The primary advantage to hyperspectral imaging is that, because an entire spectrum is acquired at each point, the operator needs no prior knowledge of the sample, and postprocessing 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 neighbourhood, allowing more elaborate spectral-spatial models for a more accurate segmentation and classification of the image.[57][58]

The primary disadvantages are 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 uncompressed hyperspectral cubes are large, multidimensional datasets, potentially exceeding hundreds of megabytes. All of these factors greatly increase the cost of acquiring and processing hyperspectral data. Also, one of the hurdles researchers have had to face 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.[9] As a relatively new analytical technique, the full potential of hyperspectral imaging has not yet been realized.

See also

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References

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[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Hyperspectral imaging

View on Grokipedia
from Grokipedia
Hyperspectral imaging (HSI) is a technique that captures and processes a broad of across hundreds of narrow, contiguous bands, typically spanning the visible to wavelengths (0.4–2.5 μm or broader), to generate a three-dimensional known as a hyperspectral cube, comprising two spatial and one . This approach enables the detailed analysis of surface materials by recording their unique signatures, which reflect how they interact with , allowing for precise identification of and physical properties at each . Unlike , which relies on a limited number of broader bands (typically 4–36), HSI provides high , often with band widths less than 10 nm, facilitating discrimination between similar materials that would otherwise be indistinguishable. The principles of HSI stem from the convergence of traditional imaging and , where is dispersed into fine channels using instruments like spectrographs or tunable filters to form contiguous narrow-band images. This results in a where each contains a complete , akin to a , which can be compared against libraries of known signatures for material classification. HSI systems operate across various platforms, including airborne sensors, satellites, and ground-based devices, with coverage extending from the (0.35 μm) to the (up to 12 μm) in advanced configurations. Hyperspectral imaging originated in the mid-1980s, with NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) serving as the first operational instrument, flown in 1987 to observe solar reflectance in contiguous bands for geological and . Initially developed for and planetary , the technology has evolved over the past four decades, incorporating advancements in miniaturization for unmanned aerial vehicles (UAVs) and satellites, enhancing accessibility and resolution. Key milestones include spaceborne missions like NASA's Hyperion on EO-1 (launched 2000), the Hyperspectral Imager for the Coastal Ocean (HICO, launched 2009 and operated until 2014) for ocean monitoring, as well as more recent examples such as NASA's EMIT (launched 2022) and Germany's EnMAP (launched 2022). HSI finds applications across diverse fields, including for mapping minerals, soils, vegetation stress, and ; for crop discrimination and yield prediction; of and harmful algal blooms; and medical diagnostics for tissue analysis and detection. In defense and , it supports , , and physiological state detection through spectral analysis. NASA's ongoing efforts, such as drone-based surveys for algal blooms in regions like , demonstrate HSI's role in real-time environmental response and data integration with satellite observations.

Fundamentals

Definition and Principles

Hyperspectral imaging is a technique that captures and processes images across a wide range of the , acquiring data in hundreds of contiguous narrow bands, typically spanning the visible to short-wave regions from approximately 400 to 2500 nm. This approach produces a three-dimensional hyperspectral , where two represent spatial information (x and y coordinates of the image) and the third encodes the information (λ) for each . Unlike traditional , which captures only a few broad bands, hyperspectral imaging enables the detailed characterization of materials by recording the full spectrum of reflected, absorbed, or transmitted at every spatial location. The core principles of hyperspectral imaging rely on the fundamental interactions of with matter, governed by phenomena such as absorption, reflection, and transmission across different of the . In the visible (400-700 nm), near-infrared (700-1000 nm), and short-wave infrared (1000-2500 nm) regions, these interactions produce unique spectral signatures that correspond to molecular compositions and structures. For instance, the reflectance spectrum of vegetation or minerals exhibits distinct patterns due to electronic transitions and vibrational modes, allowing for precise material identification. This is underpinned by the Lambert-Beer law, which describes the linear relationship between the absorption of and the concentration of absorbing species in a medium, stating that A is proportional to concentration c, path length l, and the molar absorptivity ε at a given : A=ϵclA = \epsilon c l. By measuring how light intensity decreases through or reflects off a sample, hyperspectral imaging quantifies these interactions to reveal compositional details. A key feature enabling this discrimination is the high of hyperspectral systems, which typically employs narrow bandwidths of 5-10 nm per band, allowing detection of subtle variations in molecular absorption and emission features that broader systems might overlook. This fine resolution facilitates the differentiation of materials with similar appearances but distinct chemical properties, such as various types of minerals or plant health indicators, by resolving narrow lines in the data. The resulting data cube is formed by stacking multiple two-dimensional spatial images, each captured at a specific , into a single volume; for example, a 512 × 512 image acquired across 200 bands yields a 512 × 512 × 200 containing over 50 million measurements. This structure preserves both spatial context and spectral continuity, providing a comprehensive for subsequent analysis of light-matter interactions and material properties.

Historical Development

The roots of hyperspectral imaging trace back to the , when early efforts in focused on capturing detailed spectral data for geological mapping. In the early 1980s, NASA's (JPL) developed the Airborne Imaging Spectrometer (AIS), a pioneering instrument mounted on aircraft to acquire hyperspectral data across 128 contiguous bands in the visible and near-infrared regions, primarily for mineral identification and Earth surface analysis. This system marked the transition from multispectral to hyperspectral approaches, enabling finer discrimination of materials based on their unique spectral signatures. The AIS was operational in 1983, laying the groundwork for more advanced sensors. The first fully operational hyperspectral system emerged in the late with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), deployed by in 1987 on the ER-2 aircraft. AVIRIS expanded spectral coverage to 224 bands from 400 to 2500 nm, providing high-fidelity data for environmental and geological studies, and remains in use today with upgrades. During this period, applications gained traction, particularly in the late for detecting through spectral analysis of obscured targets, as explored in early U.S. Department of Defense initiatives. 's JPL played a central role in these developments, leading instrument design and calibration efforts that bridged airborne testing to broader applications. By the , the technology expanded to spaceborne platforms, exemplified by the Hyperion instrument on 's Earth Observing-1 (EO-1) satellite, launched on November 21, 2000, which delivered 220 contiguous bands for global . Post-1990s, hyperspectral imaging shifted from predominantly and uses to domains, driven by of technologies and growing commercial interest. The saw significant advancements in portable sensors, with companies like Headwall Photonics introducing compact, field-deployable systems weighing under 5 kg and covering over 200 bands, facilitating on-site applications in and . Technologically, the field evolved from analog whiskbroom scanners in the , which captured about 100 bands, to digital pushbroom and snapshot sensors today offering over 400 bands, enabled by improvements in focal plane arrays and power that reduced times from days to hours. Recent developments through 2025 have integrated for real-time hyperspectral data analysis, with convolutional neural networks accelerating classification tasks on airborne and satellite platforms. A key milestone is the European Space Agency's (ESA) and German Aerospace Center's (DLR) EnMAP mission, launched on April 1, 2022, which provides 242 spectral bands from 420 to 2450 nm for high-resolution global , entering routine operations in November 2022. In 2024, NASA's AVIRIS-4 airborne imaging spectrometer entered service, offering improved and extended spectral coverage from 375 to 2504 nm across 338 bands.

Technology

Sensors and Hardware

Hyperspectral imaging systems rely on specialized sensors to capture high-dimensional data across numerous narrow spectral bands, typically spanning the visible, near- (VNIR), short-wave (SWIR), and mid-wave (MWIR) regions. The primary sensor types include whiskbroom (point scanners), which mechanically scan a single across the scene while dispersing spectrally; pushbroom (line scanners), which image an entire line of simultaneously and build the scene through motion; and snapshot sensors, which use 2D focal plane arrays to capture the full spatial-spectral datacube in a single exposure without scanning. Detector materials are selected based on the target spectral range, with charge-coupled devices (CCD) or complementary metal-oxide-semiconductor () arrays commonly used for VNIR (400-1000 nm) due to their high quantum efficiency and low noise. For SWIR (1000-2500 nm), (InGaAs) detectors provide extended sensitivity with cutoff wavelengths up to 2.65 μm when tuned with substrates, while (HgCdTe) is preferred for MWIR and long-wave applications owing to its tunable bandgap and high detectivity. Recent advancements as of 2024 include colloidal () detectors, such as (PbS) CQDs, enabling single-pixel detection in the near- range (1050–1630 nm) with average of 8.59 nm, superior , and lower costs compared to traditional focal plane arrays. Core hardware components encompass spectrometers for spectral dispersion and for light collection. Grating-based spectrometers employ gratings to separate wavelengths onto linear detector arrays, offering high (e.g., 5-10 nm) but requiring precise alignment to minimize aberrations. spectrometers, in contrast, use —such as Michelson or Sagnac configurations—to encode spectral information via interference patterns, enabling broader spectral coverage and higher signal-to-noise ratios (SNR) through , though they demand computational reconstruction. typically include collimators to parallelize incoming , slit apertures for spectral isolation, and bandpass filters to reject out-of-band radiation, ensuring efficient coupling to the detector. Performance metrics define sensor capabilities, with typical spectral ranges covering 400-2500 nm to encompass key molecular absorption features. Spatial resolutions vary by platform, achieving 1-30 m for airborne systems to balance coverage and detail. A high SNR, often exceeding 100:1, is essential for distinguishing subtle signatures amid noise from photon shot, read-out, or dark current sources. These sensors integrate with diverse platforms for operational flexibility. Airborne systems, mounted on drones or , enable high-resolution surveys over variable terrains; spaceborne instruments on satellites provide global monitoring with revisit times of days; and ground-based setups, such as handheld or tripod-mounted units, support close-range, real-time applications like material inspection. Accurate data requires rigorous to mitigate errors from environmental and instrumental factors. Radiometric calibration converts raw digital numbers to radiance or using integrating spheres or field references, correcting for detector response variations. Spectral calibration aligns measured wavelengths to true values via monochromatic sources like lasers, accounting for drift. Geometric calibration rectifies distortions from or platform motion, often using ground control points to ensure pixel-level accuracy. These processes collectively address atmospheric attenuation and sensor instabilities, enabling quantitative analysis.

Data Acquisition Methods

Hyperspectral imaging data acquisition methods encompass a range of techniques designed to capture both spatial and simultaneously or sequentially, enabling the formation of three-dimensional datacubes where two dimensions represent spatial coordinates and the third represents . These methods can be broadly categorized into scanning and non-scanning approaches, each balancing factors such as acquisition speed, , fidelity, and (SNR). Scanning methods typically involve mechanical or optical movement to build the datacube over time, while non-scanning or snapshot methods capture the entire scene in a single exposure, making them suitable for dynamic environments. Spatial scanning, also known as whiskbroom or point scanning, employs a mirror or scanning mechanism to direct light from individual pixels across the scene onto a spectrometer, acquiring one spatial point at a time while dispersing the for all wavelengths simultaneously. This approach uses a single detector or small , with the scanning mirror oscillating to cover the field of view pixel-by-pixel, resulting in high spectral and but requiring longer acquisition times for large scenes. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), a seminal whiskbroom instrument developed by , exemplifies this method by using a scanning mirror to produce images with 224 contiguous spectral bands at 10-20 nm resolution across 400-2500 nm, achieving high fidelity for applications. Spectral scanning techniques sequentially capture different wavelength bands across the entire spatial using tunable optical elements, allowing a fixed sensor to record images one at a time. Common implementations include acousto-optic tunable filters (AOTFs), which use in a to diffract and select specific wavelengths rapidly, and Fabry-Pérot etalons, which are interferometric cavities that tune the transmission band by adjusting the cavity length via voltage or piezoelectrics. AOTFs enable fast switching (microseconds) for applications like real-time biomedical , while Fabry-Pérot etalons provide narrow bandpass control (down to 10 nm) for high-resolution spectral selectivity in the . These methods are advantageous for static scenes but can introduce artifacts from temporal changes during sequential acquisition. Non-scanning or snapshot methods acquire the full spatiospectral datacube in a single integration period, ideal for fast-moving or time-varying scenes such as in video-rate applications. One prominent example is the computed spectrometer (CTIS), which disperses the scene's light through a diffractive optical element onto a focal plane array, encoding spatial and spectral information into a two-dimensional dispersed image that is computationally reconstructed into a hyperspectral cube. Mosaic filter arrays, akin to filters but with multiple narrowband interference filters deposited on sensor pixels, enable compact, video-rate hyperspectral by capturing interleaved spatial-spectral data directly, though at the cost of reduced per-band resolution. Emerging compressive sensing approaches, such as single-pixel using filters, further enhance non-scanning capabilities by reconstructing hyperspectral data from modulated measurements, achieving high SNR in the NIR with reduced hardware complexity. These techniques support frame rates up to 30 Hz or higher, facilitating applications in and . Spatiospectral scanning, often exemplified by pushbroom methods, combines line-wise spatial scanning with spectral dispersion to acquire one spatial line of the scene at a time, where each pixel in the line has its full spectrum measured simultaneously via a dispersive element like a prism or grating. As the sensor platform moves forward (e.g., in airborne systems), successive lines build the image, offering a compromise between speed and resolution by utilizing linear detector arrays. This hybrid approach is widely used in satellite and UAV-based systems for efficient coverage of large areas, such as environmental monitoring, with acquisition speeds enhanced by the platform's motion. Trade-offs among these methods primarily revolve around acquisition speed, resolution, and SNR; for instance, scanning techniques like whiskbroom provide superior SNR and resolution (e.g., >1000:1 in AVIRIS) but are slower, taking seconds to minutes per frame, whereas snapshot methods excel in speed for dynamic scenes yet suffer lower SNR due to dilution across dispersed elements or reduced per-band exposure. Pushbroom and spectral scanning offer intermediate performance, with pushbroom favoring extended spatial coverage at the expense of cross-track resolution uniformity. Selection depends on the application, such as high-fidelity static mapping via whiskbroom versus real-time via snapshot CTIS.

Hyperspectral vs. Multispectral Imaging

Hyperspectral imaging and both capture spectral data across multiple wavelengths but differ fundamentally in resolution and data granularity. Hyperspectral imaging acquires data in hundreds of contiguous narrow spectral bands, typically 200 or more bands with widths of around 10 nm, enabling detailed spectral signatures akin to . In contrast, uses a smaller number of discrete broader bands, often 3 to 10 bands with widths ranging from 50 to 200 nm, as seen in systems like the Landsat satellites. These differences arise from the design of sensors: hyperspectral systems scan or snapshot across fine intervals in the , while multispectral systems filter into predefined coarse channels. Historically, predates hyperspectral techniques, with roots in early 20th-century photography experiments and widespread adoption in the 1960s through for . NASA's in 1972 marked a key milestone with its four-band Multispectral Scanner, focusing on broad mapping. Hyperspectral imaging emerged in the 1970s from field efforts supporting Landsat analysis, with NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in 1987 extending multispectral methods to provide spectroscopy-like detail for material identification. The richer data from hyperspectral imaging allows for fine-grained material discrimination, such as distinguishing specific types of crop stress through unique spectral absorption features, whereas supports coarser classifications like general types or health. This enhanced resolution in hyperspectral data stems from its contiguous bands, which capture subtle variations in reflectance that broad multispectral bands average out. Hyperspectral imaging generates significantly larger data volumes, often gigabytes per scene due to the high number of bands and pixels, imposing greater computational demands for storage, , and analysis compared to the more efficient multispectral data suitable for broad-scale monitoring. Specialized algorithms and hardware are typically required to handle hyperspectral datasets, while multispectral can often occur in near real-time with standard tools.
AspectHyperspectral Imaging
Number of Bands200+ contiguous bands3–10 discrete bands
Band Width~10 nm50–200 nm
Data Volume per SceneGigabytesMegabytes
Primary StrengthFine material discriminationEfficient broad classification
Multispectral imaging can be derived as a subset of hyperspectral data by binning or averaging adjacent narrow bands into broader ones, allowing flexibility in analysis workflows.

Hyperspectral vs. Other Imaging Modalities

Hyperspectral distinguishes itself from traditional RGB and panchromatic by capturing hundreds of narrow, contiguous bands, typically spanning the visible to short-wave (VNIR-SWIR) range, enabling detailed material identification through unique signatures, whereas RGB approximates human vision with only three broad color bands (, , ) and panchromatic integrates a wide range into a single intensity without differentiation. This added depth in hyperspectral systems allows for precise discrimination of materials like minerals or types that appear indistinguishable in RGB or panchromatic images, which are limited to visual or broad intensity representations. In contrast to thermal imaging, hyperspectral imaging primarily analyzes reflected or emitted in the VNIR-SWIR regions (400–2500 nm) to reveal compositional properties, while imaging operates in the long-wave (8–14 µm) to detect surface temperatures and heat emissions, providing complementary but distinct data on thermal characteristics rather than spectral reflectance. Similarly, hyperspectral imaging focuses on two-dimensional spectral-spatial information, whereas ( Detection and Ranging) uses pulses in the near- to generate three-dimensional structural data, such as and canopy , without inherent spectral content. These modalities thus address different physical properties: hyperspectral for biochemical composition and / for thermal or geometric attributes. Hyperspectral imaging typically achieves a moderate spectral resolution of 5–20 nm per band, balancing detail and practicality, while ultraspectral imaging employs finer resolutions below 5 nm (often ~1 nm) across thousands of bands for ultra-precise spectral analysis, though its rarity stems from increased instrumental complexity, data volume, and processing demands. Hyperspectral imaging is frequently fused with other modalities to enhance overall analysis, such as combining it with data to integrate spectral composition with structural height information for applications like vegetation mapping, where hyperspectral identifies and LiDAR measures canopy architecture. A key limitation of hyperspectral imaging lies in its confinement to optical wavelengths (primarily –2500 nm for VNIR-SWIR, extendable to infrared), excluding non-optical regions like microwaves, where systems excel in all-weather penetration and subsurface imaging that hyperspectral cannot achieve without specialized extensions.

Applications

Environmental and Earth Sciences

Hyperspectral imaging plays a pivotal role in environmental and Earth sciences by enabling detailed monitoring of natural ecosystems through the analysis of spectral signatures across hundreds of narrow bands. In vegetation mapping, it facilitates the detection of absorption features around 680 nm, which is crucial for assessing forest health and . This absorption band, part of the region (680–760 nm), allows for precise estimation of content, indicating stress levels, nutrient status, and in forest canopies. For instance, hyperspectral imagery has been used to validate and enhance large-scale maps of content in forests, supporting assessments by differentiating vegetation types based on subtle spectral variations. In mineral exploration, hyperspectral imaging identifies ore signatures by targeting specific absorption features in the short-wave infrared (SWIR) region, such as the 2200 nm band for kaolinite, a common alteration mineral associated with mining deposits. Airborne systems like AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) have been instrumental in mapping these signatures over large areas, delineating mineralized zones and host rocks with high accuracy. Studies using AVIRIS-NG data in geological provinces have demonstrated its potential for geo-exploration at scales of 1:10,000 to 1:15,000, revealing surface indicators of mineralization that guide targeted drilling and reduce exploration costs. Quantitative estimation of clay minerals via the depth of the 2200 nm absorption band has shown reliable results in mine environments, correlating spectral data with ground-validated concentrations. For water quality assessment in coastal zones, hyperspectral imaging detects algal blooms by exploiting phycocyanin absorption peaks at approximately 620 nm, a pigment unique to . This capability allows for early warning of harmful blooms that affect marine ecosystems, with algorithms like the three-band phycocyanin index using hyperspectral to map concentrations in turbid waters. In coastal validation sites, instruments such as EMIT and PACE/OCI have identified through distinct absorption features around 620 nm, enabling spatiotemporal monitoring of bloom dynamics and informing mitigation strategies. Such applications highlight hyperspectral imaging's superiority in distinguishing from chlorophyll-a absorption (665–681 nm), providing accurate bloom extent maps over large areas. Soil analysis benefits from hyperspectral imaging in mapping erosion and contamination, particularly through SWIR bands that reveal heavy metal signatures via altered reflectance patterns. For erosion monitoring, hyperspectral data integrated with ground measurements identify indicators like soil texture and organic matter loss, enabling the characterization of erosion-prone areas at high resolution. In contamination studies, SWIR hyperspectral sensors (e.g., with 10 nm resolution) estimate heavy metal concentrations such as cadmium and lead by correlating spectral features with lab-validated levels, supporting large-scale pollution mapping. Airborne hyperspectral imagery has been used to generate models for heavy metal distribution in agricultural soils, reducing the need for invasive sampling and aiding remediation efforts. In climate monitoring, hyperspectral imaging supports the detection of glacier algae and estimation of carbon sinks using satellite platforms like PRISMA, launched in 2019. PRISMA data retrieve snow and ice properties, including algal blooms on glaciers, by analyzing spectral shifts in the visible-near infrared range caused by pigments like in . This enables quantification of algal , which darkens ice surfaces and accelerates melt, contributing to feedback models. For carbon sink assessment, PRISMA hyperspectral imagery estimates organic carbon content through multitemporal analysis of and spectra, providing insights into terrestrial carbon storage dynamics at regional scales. Such applications underscore hyperspectral imaging's role in tracking climate-sensitive processes like glacier algae proliferation and .

Agriculture and Food Processing

Hyperspectral imaging has become integral to , enabling non-invasive monitoring of crop health and optimization of resource use. In nutrient deficiency detection, particularly for , hyperspectral sensors capture changes in the red-edge around 700 nm, where absorption shifts due to stress, allowing farmers to apply targeted fertilizers and reduce overuse. Studies using airborne hyperspectral systems have achieved high accuracy in estimating canopy content, with correlations exceeding 0.8 between spectral indices and ground measurements in crops like corn and . For yield prediction, hyperspectral data from unmanned aerial vehicles (UAVs) integrates vegetation indices such as the (NDVI) and models to forecast harvests weeks before maturity, improving planning in fields like and snap beans. Pest and disease management benefits from hyperspectral imaging's ability to detect early spectral anomalies in leaf reflectance, often before visual symptoms appear. Fungal infections, such as bacterial leaf spot in tomatoes, produce distinct signatures in the near-infrared (NIR) bands due to altered water content and cellular damage, enabling classification accuracies up to 100% in UAV-based surveys. This early identification supports timely interventions, minimizing yield losses in crops like tea and potatoes, where hyperspectral analysis differentiates biotic stresses from abiotic ones using support vector machines on selected wavebands. In food processing, hyperspectral imaging facilitates inline quality control by detecting contaminants and assessing attributes non-destructively. Foreign materials in grains, such as mycotoxins or plastics, are identified through unique NIR absorption patterns around 1400-1600 nm, supporting rapid sorting in high-throughput lines with detection rates above 95%. Ripeness evaluation in fruits relies on spectral correlations with internal sugar content; for instance, in citrus and strawberries, models using visible-NIR bands predict soluble solids content with R² values over 0.9, aiding harvest timing and reducing waste. Quality sorting extends to dairy products, where hyperspectral systems measure milk fat adulteration via absorption at 1700 nm, integrating into supply chains for real-time verification post-2010s advancements in portable sensors. Drone-based hyperspectral scouting has emerged as a scalable example, mapping field variability for integrated agro-food systems and enhancing traceability from farm to processor.

Biomedical and Health Applications

Hyperspectral imaging (HSI) has emerged as a powerful tool in biomedical applications, particularly for non-invasive tissue analysis and disease detection by capturing detailed spectral signatures across hundreds of narrow wavelength bands. In medical diagnostics, HSI enables the differentiation of pathological tissues based on biochemical compositions, such as variations in hemoglobin oxygenation, which differ between healthy and cancerous regions. For instance, in endoscopy, HSI systems map relative hemoglobin concentration and saturation to identify tumors, leveraging absorption peaks of oxygenated hemoglobin at approximately 540 nm and 577 nm, where malignant tissues exhibit distinct dips in reflectance due to increased vascularity and altered oxygenation. A clinically translatable hyperspectral endoscopy system has demonstrated high sensitivity in discriminating blood oxygen saturation levels in gastrointestinal tissues, aiding real-time cancer detection during procedures. In wound monitoring, HSI assesses infection status by analyzing bacterial spectra in the near-infrared (NIR) range, where microbial signatures provide indicators of contamination without invasive sampling. NIR hyperspectral data can quantify tissue oxygenation and perfusion changes, essential for evaluating progress, with studies showing reliable detection of common pathogens like Staphylococcus aureus through fluorescence hyperspectral imaging combined with algorithms. Systematic reviews highlight HSI's role in measuring deoxygenated and water content in chronic wounds, enabling early infection identification and personalized treatment adjustments. Ophthalmology benefits from HSI in retinal disease mapping, such as age-related (), where fundus imaging reveals spectral biomarkers of and pigment alterations. Hyperspectral retinal imaging non-invasively captures autofluorescence and reflectance variations, providing insights into retinal ischemia and neurodegeneration, with potential for early diagnosis via mapping. In , HSI classifies skin lesions by exploiting absorption in the visible-NIR , distinguishing from benign nevi through elevated and signals in malignant areas. Automated hyperspectral dermoscopy has achieved higher sensitivity than standard methods for detection, supporting non-invasive guidance. Non-invasive diagnostics extend to blood glucose estimation using HSI via diffuse reflectance spectroscopy, which analyzes light scattering from skin to infer glucose levels without puncturing the tissue. Portable HSI devices, developed in the 2020s, integrate machine learning to process NIR spectra for point-of-care monitoring, showing promising accuracy in preliminary clinical studies with predictions falling within clinically acceptable ranges in error grid analyses. These compact systems facilitate real-time diabetes management, with diffuse reflectance models predicting glucose concentrations by isolating glucose-specific spectral features amid interferents like water and hemoglobin.

Industrial and Engineering Uses

Hyperspectral imaging plays a pivotal role in industrial and applications by enabling non-destructive, high-resolution analysis of materials and structures based on their unique spectral signatures. In and , it facilitates precise identification of material compositions, supporting and . Engineering uses extend to monitoring, where spectral data reveal subtle degradation processes invisible to the . Surveillance applications leverage hyperspectral capabilities to detect anomalies in complex environments, originating from developments that emphasized target discrimination under . In waste sorting facilities, hyperspectral imaging excels at differentiating plastic types for efficient . For instance, shortwave (SWIR) hyperspectral systems operating around 1700 nm detect C-H bond absorptions that distinguish (PET) from (HDPE), allowing automated sorting of post-consumer waste streams with high accuracy. This approach has been integrated into industrial lines to process mixed polyolefins, reducing contamination in recycled outputs and improving material purity. Studies demonstrate sorting efficiencies exceeding 90% for common plastics like PET and HDPE when using variable selection methods on hyperspectral data. Surveillance applications, rooted in hyperspectral , utilize the technology for detection and anomaly spotting in contexts. Hyperspectral sensors capture fine spectral differences between concealed targets and backgrounds, enabling detection of vehicles or personnel under netting or foliage that evades imaging. Early tests confirmed hyperspectral data's utility in discriminating actual targets from decoys by analyzing and fabric spectra, with ongoing adaptations for enhancing threat identification in urban or settings. In , hyperspectral imaging assesses material degradation in like bridges and pavements. For bridges, it identifies early-stage through spectral shifts associated with iron oxides, often before visual signs appear, using indices like the Corrosion Severity Index derived from multi-band . Remotely piloted systems equipped with hyperspectral cameras have mapped on girders with sub-millimeter precision, aiding . Pavement analysis employs hyperspectral data to detect cracks and surface distress by characterizing asphalt composition and moisture content, with spectral descriptors improving autonomous accuracy over traditional methods. Hyperspectral imaging supports purity checks in and pharmaceuticals by comparing sample spectra against reference libraries. In , it maps compositions to assess purity, identifying minerals and estimating grades in tungsten-tin deposits with classifiers achieving over 85% accuracy. Spectral libraries enable non-contact verification of quality during extraction. In pharmaceuticals, UV hyperspectral imaging characterizes tablet compositions, detecting active ingredients and polymorphs via unique absorption features, which supports in-line and detection. Integration with zoology aids animal tracking and behavior studies by analyzing spectral camouflage in wildlife. Hyperspectral imaging quantifies how species like cuttlefish match environmental spectra, revealing effective color camouflage in predator-prey dynamics through full-spectrum reflectance data. This has been applied in field studies to track camouflaged animals in natural habitats, enhancing understanding of behavioral adaptations without disturbance.

Astronomy and Space Exploration

Hyperspectral imaging plays a crucial role in planetary mapping by enabling detailed analysis of surface compositions, which reveal geological histories and potential . On Mars, the Compact Imaging Spectrometer for Mars (CRISM), a visible-to-near-infrared hyperspectral imager aboard the (MRO), has mapped diverse phyllosilicates such as and Fe/Mg-smectites in regions like Mawrth Vallis, indicating widespread ancient aqueous alteration processes. These detections, achieved through spectral signatures in the 0.4–2.6 μm range, have guided rover landings and refined models of Mars' hydrological past. In asteroid and comet exploration, hyperspectral techniques facilitate in situ spectral classification to evaluate resource potential and compositional origins. The Hayabusa2 mission to the carbonaceous asteroid Ryugu (launched in 2014) employed the MicrOmega near-infrared hyperspectral microscope, which resolved mineral phases like phyllosilicates and carbonates at micron scales during surface operations, confirming Ryugu's primitive, aqueously altered composition akin to CI chondrites. This data, combined with returned sample analyses, has informed assessments of volatile and organic content for future resource utilization. Ground-based hyperspectral imaging supports atmosphere characterization via transmission spectroscopy, particularly for detecting molecular species like . Instruments such as the Transmission Imager (ETSI), a low-resolution slitless with multi-band imaging capabilities, enable rapid ground-based observations of transiting , resolving features indicative of H₂O absorption in the near-infrared. For instance, such methods have contributed to detections in hot Jupiters like HD 209458 b, where near-infrared transmission spectra show absorption bands around 1.4 μm. In spaceborne applications, the Earth Surface Mineral dust source Investigation (EMIT) hyperspectral imager on the (deployed in 2022) extends these techniques to orbital mineral mapping, measuring visible-to-shortwave infrared spectra (380–2500 nm) to track dust sources and compositions globally. A primary challenge in ground-based hyperspectral astronomical observations is correcting for atmospheric interference, including turbulence-induced blurring and telluric absorption lines that overlap target spectra. systems on large telescopes mitigate wavefront distortions in real-time, improving for hyperspectral data cubes, while models remove telluric effects to isolate planetary signals. These corrections are essential for accurate spectral unmixing in direct imaging scenarios, where star-planet contrasts demand high fidelity.

Data Processing and Analysis

Spectral Data Handling

Hyperspectral imaging generates three-dimensional s that capture spatial and spectral dimensions, often resulting in massive datasets requiring careful preprocessing to ensure accuracy and usability. Preprocessing typically begins with atmospheric correction to remove effects from and absorption by atmospheric constituents, such as aerosols and gases. The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) model, based on MODTRAN simulations, is a widely used first-principles approach for correcting hyperspectral data in the visible to shortwave range, enabling retrieval of surface reflectance spectra. Radiometric calibration follows to convert raw digital numbers from the into physical units like radiance or reflectance, accounting for sensor response variations and ensuring quantitative analysis; this step often involves or on-board sources to relate counts to incident . Geometric registration aligns the spatially, correcting for distortions due to motion, , or platform instability, which is crucial for multi-temporal or multi-sensor fusion; techniques include feature-based matching or optimization methods to achieve sub-pixel accuracy in long-wave hyperspectral imagery. Data reduction addresses the high dimensionality of hyperspectral cubes, where hundreds of contiguous bands lead to redundancy and computational burdens from terabyte-scale volumes in large-scale acquisitions. This high dimensionality can introduce multicollinearity, noise, and increased risk of overfitting, potentially degrading model performance in quantitative applications. is a seminal method for , transforming the data into orthogonal components ordered by variance to retain key spectral information while discarding -dominated bands; for instance, PCA can reduce a 200-band cube to 5-10 components with minimal information loss, facilitating efficient storage and analysis. Band selection serves as another key dimensionality reduction technique, selecting a subset of the most informative bands while eliminating redundant, correlated, or irrelevant ones. This approach preserves physical interpretability of specific wavelengths and mitigates issues such as multicollinearity, computational burden, and overfitting associated with full-spectrum data. It is especially advantageous in applications like detecting heavy metals in soil, where selected bands focus on wavelengths most sensitive to related soil properties (e.g., organic matter, iron oxides), reducing redundancy and processing costs while improving prediction accuracy. Studies demonstrate that selected band models outperform full-spectrum models; for example, in lead (Pb) inversion, optimized band selection achieved R² values up to 0.75, compared to 0.43 for full-spectrum approaches. mitigation is essential to preserve fine spectral features amid sensor or environmental interference, with the Savitzky-Golay filter providing effective smoothing through local polynomial least-squares fitting that avoids distorting absorption peaks; this technique enhances signal quality in hyperspectral preprocessing by reducing random while maintaining . Storage and management of hyperspectral data cubes demand formats that support multidimensional arrays and metadata. The ENVI format, with its binary image files (.dat or .img) and header (.hdr) describing band interleave, wavelengths, and projections, is a standard for hyperspectral processing due to its compatibility with software. HDF5 ( version 5) is preferred for large-scale archiving, offering hierarchical structure, compression, and efficient access to hyperspectral cubes via libraries like netCDF-4, as used in datasets from and AVIRIS. Compression techniques like JPEG2000 are applied to reduce file sizes without significant loss, leveraging transforms for both spatial and spectral decorrelation; when combined with PCA, it achieves high compression ratios for hyperspectral imagery while preserving endmember variability for downstream tasks. Handling these voluminous datasets presents challenges, including storage demands and processing times for cubes exceeding terabytes in airborne or missions. GPU has emerged post-2010 as a solution, enabling parallel computation for preprocessing steps like compression and , with implementations achieving real-time performance in embedded hyperspectral systems.

Analysis Techniques and Algorithms

Analysis techniques and algorithms in hyperspectral imaging focus on extracting meaningful information from high-dimensional cubes, enabling the identification of materials, types, and physiological states at sub-pixel resolutions. These methods operate on preprocessed to address challenges such as variability, mixed pixels, and noise, often assuming a linear mixing model for sub-pixel compositions. Key approaches include unmixing for abundance estimation, for thematic mapping, for outliers, for advanced feature extraction, and quantitative indices for specific applications like vegetation monitoring. Spectral unmixing decomposes the observed of a mixed into a set of pure endmember spectra and their corresponding fractional abundances, which is essential for resolving sub-pixel materials in scenes with heterogeneous coverage. The linear mixing model, a foundational assumption, posits that the A\mathbf{A} is a of endmember spectra M\mathbf{M} weighted by abundance fractions S\mathbf{S}, expressed as A=MS+n\mathbf{A} = \mathbf{M} \mathbf{S} + \mathbf{n}, where n\mathbf{n} accounts for noise; abundances are typically estimated using optimization to ensure non-negativity and sum-to-one conditions. This model facilitates applications like mineral mapping in , where unmixing reveals proportions of components invisible to coarser resolutions. Seminal work has emphasized fully methods to improve accuracy in endmember variability scenarios. Classification algorithms assign pixels to predefined or discovered categories based on spectral signatures, supporting tasks such as land cover delineation. Supervised methods, like the Spectral Angle Mapper (SAM), measure similarity between a pixel spectrum and reference endmembers by computing the angle θ\theta in n-dimensional space, where cos⁡θ=x⋅y∣∣x∣∣ ∣∣y∣∣\cos \theta = \frac{\mathbf{x} \cdot \mathbf{y}}{||\mathbf{x}|| \ ||\mathbf{y}||}, with x\mathbf{x} and y\mathbf{y} as pixel and reference vectors; smaller angles indicate higher similarity, making SAM robust to illumination variations. Unsupervised techniques, such as ISODATA (Iterative Self-Organizing Data Analysis Technique), iteratively cluster pixels by minimizing intra-cluster variance through splitting and merging operations, starting from initial centroids and refining based on spectral distances; it is particularly useful for exploratory analysis in unknown environments like forest inventories. These methods achieve classification accuracies exceeding 85% on benchmark datasets when combined with spatial context. Anomaly detection identifies pixels that deviate spectrally from the surrounding background, crucial for detecting rare events like mineral deposits or defects. The Reed-Xiaoli (RX) algorithm, a benchmark statistical approach, models the background as a multivariate Gaussian and computes anomalies using the Mahalanobis distance d2(x)=(x−μ)TΣ−1(x−μ)d^2(\mathbf{x}) = (\mathbf{x} - \boldsymbol{\mu})^T \mathbf{\Sigma}^{-1} (\mathbf{x} - \boldsymbol{\mu}), where x\mathbf{x} is the pixel vector, μ\boldsymbol{\mu} the mean, and Σ\mathbf{\Sigma} the covariance; global or local variants adapt to non-stationary scenes, with thresholds set via constant false alarm rates. RX has demonstrated superior performance in real-time hyperspectral surveillance, outperforming subspace methods in low-signal scenarios. Since 2015, has integrated deeply with hyperspectral analysis, enhancing feature extraction from high-dimensional data through techniques like support vector machines (SVMs) for kernel-based and convolutional neural networks (CNNs) for spatial-spectral fusion, achieving over 90% accuracy on datasets like Indian Pines. More recent advances as of 2025 include architectures for improved and models for hyperspectral image processing and unmixing, which leverage generative approaches to handle noise and variability more effectively. SVMs map spectra to higher dimensions for nonlinear separation, while CNNs, such as 3D variants, learn hierarchical features directly from datacubes, reducing reliance on handcrafted indices and improving across sensors. Hyperspectral libraries, notably the USGS Spectral Library, provide reference spectra for matching and training, containing over 2,500 measured signatures of minerals, vegetation, and soils to validate ML outputs in applications like crop stress detection. Quantitative analysis employs specialized indices derived from hyperspectral bands to estimate biophysical parameters, extending broadband metrics like the (NDVI) to finer resolutions. The Photochemical Reflectance Index (PRI), calculated as PRI=R531−R570R531+R570\text{PRI} = \frac{R_{531} - R_{570}}{R_{531} + R_{570}} where RλR_{\lambda} is at λ\lambda, tracks xanthophyll cycle activity and serves as a proxy for , correlating strongly (r > 0.8) with light-use efficiency in diverse ecosystems. Hyperspectral extensions of NDVI incorporate multiple red-edge bands for improved sensitivity to content, enabling precise monitoring of photosynthetic performance in .

Advantages and Limitations

Key Advantages

Hyperspectral imaging provides high specificity for material identification by capturing the unique signatures, or "fingerprints," of substances across hundreds of narrow bands, allowing non-contact discrimination of materials that may appear similar in broadband imaging. This capability has demonstrated high accuracies in intelligent material classification tasks using algorithms on hyperspectral data. For instance, in mineral identification, advanced models achieve up to 95.73% accuracy in classifying minerals from hyperspectral images, enabling precise mapping of geological features without physical sampling. A key benefit is its non-destructive nature, as the technique relies on to analyze samples without physical contact, preserving delicate or irreplaceable items. In art conservation, hyperspectral imaging generates accurate digital records of paintings and artifacts, facilitating pigment identification and monitoring of degradation over time without altering the original material. Similarly, in biomedical applications, it examines tissues non-invasively, supporting diagnostics while maintaining sample integrity. The technology yields rich, multidimensional datasets that support the creation of extensive hyperspectral libraries, serving as global databases for reference and comparison. These libraries, such as the USGS Global Hyperspectral Imaging , compile spectra from diverse sources including ground-based, airborne, and spaceborne platforms, enabling standardized worldwide. Such resources are invaluable for training models, enhancing applications in and across fields like and . Hyperspectral imaging exhibits remarkable versatility, operable across a wide range of scales from microscopic to -based observations. At the microscale, hyperspectral microscopy reveals chemical compositions and structural details in biological samples or materials, while at the macroscale, systems monitor large-area phenomena like health or deposits. This scalability stems from adaptable sensor designs, making it suitable for both and environments. Furthermore, it enables quantitative measurements of material properties, such as chemical concentrations, through inversion of spectral data using models. These models simulate light-matter interactions to retrieve parameters like atmospheric pollutant levels or content directly from radiance measurements, providing verifiable concentrations without indirect proxies. For example, in , such approaches accurately quantify emissions like NO2 and SO2 from sources using hyperspectral observations.

Challenges and Disadvantages

Hyperspectral imaging produces enormous volumes of data due to the high spectral and spatial resolutions involved, often resulting in datacubes that demand substantial storage and computational power. For instance, acquiring hyperspectral imagery over a 1 km² area at 1 m with 200 spectral bands and 12-bit depth can generate approximately 0.3 GB of data, creating significant processing bottlenecks that hinder real-time analysis and efficient handling in resource-constrained environments. The high dimensionality of these datasets exacerbates challenges in storage, transmission, and computational , particularly for large-scale applications. The technology's cost and operational complexity further limit its adoption. Portable hyperspectral sensors, especially those operating in the short-wave range, typically cost between $45,000 and $90,000 or more, making them inaccessible for many users outside well-funded or industrial settings. Moreover, effective deployment requires specialized expertise in to interpret the intricate spectral signatures and manage data calibration, as misapplications can lead to inaccurate results without proper . Atmospheric interference poses a major hurdle, as elements like absorb in specific bands, such as around 1400 nm, which distorts target measurements and requires sophisticated atmospheric correction techniques to achieve reliable data. Illumination variability compounds this issue, with fluctuations in lighting—due to time of day, , or angle—causing shifts and reduced reproducibility, thereby complicating comparative analyses across multiple acquisitions. As of 2025, emerging challenges include implications in applications, where hyperspectral capabilities to detect compositions could enable intrusive monitoring of private spaces, raising ethical and regulatory concerns in . Scalability for real-time processing on drones remains problematic, as the high data volume overwhelms onboard computing resources, limiting applications in dynamic environments like or . Recent advancements, such as low-cost DIY hyperspectral devices under $10,000 and AI-based , are beginning to address cost and real-time constraints as of November 2025. Some analysis techniques, such as , help mitigate data volume issues but do not fully resolve real-time constraints.

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