CSIRO  Earth Observation Centre

  An Overview of
Hyperspectral Remote sensing

1.  Imaging Spectroscopy

1.1  Principles of Spectroscopy

The distribution of energy as a spectrum over the wavelength of electromagnetic radiation emitted by a luminous object contains information that can be received over great distances. Since the discovery of the composite nature of white light by Newton in 1664 the physical principles of spectroscopy in all ranges of wavelengths of EMR have been used to study the properties of terrestrial and extraterrestrial objects (Sturm, 1992). The recent demand for powerful systems for studying the environment and its local and global changes, has led to the application of spectroscopy in earth resource mapping and monitoring. 

With the advances in computer and detector technology, the new field of imaging spectroscopy has developed (Goetz et al., 1985; Green et al., 1990; Vane, Duval, Wellman, 1993; Merton and Cochrane, 1995). Imaging spectroscopy is a new technique for obtaining a spectrum in each position of a large array of spatial positions so that any one spectral wavelength can be used to make a coherent image (datacube). The image may be of a vegetation sample in the laboratory, a field study site from an aircraft, or a whole planet from a spacecraft or Earth-based telescope (Clark, 1998). Imaging spectroscopy for remote sensing involves the acquisition of image data in many contiguous spectral bands with an ultimate goal of producing laboratory quality reflectance spectra for each pixel in an image (Goetz, 1992b).

Figure 1.  224 Band AVIRIS Datacube Of Jasper Ridge (Calif.).

Note: x and y axes represent spatial data (1024 x 614) as a 3 band colour composite image (R = band 43, G = 17, B = 10). The z axis represents spectral data as 224 contiguous bands from 0.4mm (foreground) to 2.5mm (background) in pseudocolour (rainbow). Dataset: two combined 30 April 1994 ATREM calibrated reflectance cubes (281Mb) centred on Jasper Ridge, prior to spatial subsetting to the standard 512 x 614 pixels.

Currently, spectrometers are in use in the laboratory, field, aircraft, and on satellites (looking at the Earth, and space). Imaging spectroscopy is based on the interaction and reflectance of photons with molecular structures of surface materials. Reflectance and emittance spectroscopy of natural surfaces are sensitive to specific chemical bonds in materials, whether solid, liquid or gas. Spectroscopy has the advantage of being sensitive to both crystalline and amorphous materials, unlike some diagnostic methods such as X-ray diffraction. The other main advantage is that spectroscopy can be used at the microscopic scale (laboratory) and to macroscopic scales (earth remote sensing and planetary astronomy).

Because spectroscopy is sensitive to so many processes, spectra can be very complex. However, it is because of this sensitivity that spectroscopy has great potential as a diagnostic tool. Reflectance spectroscopy can be used without sample preparation, is non-destructive, and can be carried out remotely from airborne and satellite sensors.

Natural targets are usually illuminated by the whole hemisphere of the sky, and therefore receive both direct solar flux and scattered skylight. A proportion of the incident radiation is reflected, either directly from the surface, or after multiple interactions within the surface if the material is translucent to the incoming radiation. Natural objects are generally not perfectly diffuse (Lambertian) reflectors, and therefore the intensity of the reflection varies with the angle that it leaves the surface. Consequently, the radiation environment comprises two hemispherical distributions of electromagnetic radiation, one incoming and one outgoing (Clarke, 1998). These interactions of absorption and reflection form the basis of spectroscopy and hyperspectral analysis.

Imaging spectroscopy was originally developed to obtain geochemical information from inaccessible planetary surfaces within the solar system (Goetz, 1992a). However, the primary application of this new technique has currently shifted towards the observation of the Earth with hyperspectral sensors, such as AVIRIS. The interaction of electromagnetic radiation with materials on a Îmacroscopicâ level (Goetz, 1992a, p2), including refraction, diffraction, and scattering effects, formed the basis of traditional remote sensing theory. However, hyperspectral research and development has shifted the emphasis towards monitoring interactions of electromagnetic energy within molecules, crystal lattices, and cell structures (Nassau, 1980; Goetz, 1992a). This new perspective requires knowledge of quantum mechanics and the application of a Îparticulate viewâ (Goetz, 1992a, p2) of electromagnetic energy.

Geology for instance is one of the first disciplines to benefit from imaging spectroscopy and most theory is developed relative to earth material interactions. Increasingly, vegetation-based research is utilising tools and techniques developed from imaging spectroscopy for geology. Ecological research in particular, is likely to benefit rapidly from the increased spectral resolution that imaging spectroscopy can provide.

Most natural Earth surface materials have diagnostic absorption features in the 0.4 -2.5mm range of the reflected spectrum. Since the diagnostic features for each material are apparent over very narrow spectral bands, differences between materials can only be identified if the spectrum is sampled at a sufficiently high resolution. Imaging spectrometers have been recently developed as a new generation of airborne optical remote sensing systems specifically designed to acquire this hyperspectral information.


Figure 2.  Imaging Spectroscopy Concept (Source: NEMO).

Variations in material composition has potential to produce shifts in the position and shape of absorption bands in the spectrum. With the diverse chemistry of the materials, spectral signatures can be complex and sometimes difficult to interpret. However, this effect is now decreasing with the increasing knowledge of the variation in spectral features. Clark (1998) suggests that for imaging spectroscopy Îthe previous disadvantage is turning into a huge advantage, allowing us to probe ever more detail about the chemistry of our natural environmentâ (Clarke, 1998 p8).


1.2  Nomenclature of Spectroscopy

Imaging spectroscopy has many names in the remote sensing community, including imaging spectrometry, hyperspectral imaging, and ultraspectral imaging. Spectroscopy is the study of electromagnetic radiation. Spectrometry is derived from spectro-photometry, the measure of photons as a function of wavelength, a term used in astronomy (Clarke, 1998). However, spectrometry is becoming a term used to indicate the measurement of non-light quantities, such as in mass spectrometry (Ball, 1995). Terms like laboratory spectrometer, spectroscopist, reflectance spectroscopy, and thermal emission spectroscopy are in common use in this sub-discipline of remote sensing. Measurement-related terms such as spectrometrist and reflectance spectrometry are now rarely used. Therefore terminology consistent with "imaging spectroscopy" is becoming the standard (Clark, 1998).

"Hyper" means excessive, but analysts of imaging spectrometer data sparingly remove bands from datacubes. Hyperspectral datasets generally contain at least 16 contiguous bands of high spectral resolution over a region of the electromagnetic spectrum. Ultraspectral ("beyond hyperspectral") is used to describe datasets containing thousands of bands. Although no such sensor has been designed with this complexity, it is regarded as the future of imaging spectroscopy.

Source (...incl references therein):

    Merton, R. N. (1999). Multi-temporal analysis of community scale vegetation stress with imaging spectroscopy. (Ph.D. Thesis), Geography Department, University of Auckland, New Zealand. 492p.


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