30
1: Richard Lucas, Aled Rowlands, Olaf Niemann, Ray Merton
Table 1.5. Commercially available software for hyperspectral applications
Software
Environment for Visualizing Images (ENVI)
EASI-PACE
Imagine
Hyperspectral product Generation System
(HPGS)
Hyperspectral Image Processing
and Analysis System (HIPAS)
Spectral Image Processing System (SIPS)
1.6
Applications
Developer/Distributor
Research Systems Inc., USA
PCI Geomatics, Canada
ERDAS, USA
Analytical Imaging and Geophysics, USA
Chinese Academy of Sciences, China
University of Colorado, USA
Although imaging spectrometry has been used in military applications (e. g.,
distinguishing between camouflage and actual vegetation) for many years,
the classified nature of the information has resulted in few published papers
regarding their origins (van der Meer and de Jong 2001). Therefore, as hyperspectral remote sensing data became available to the civilian community,
the early phases of analysis focused largely on understanding the information
content of the data over a disparate range of environments. Subsequently, the
development of algorithms specifically designed to more effectively manipulate the enormous quantities of data generated became a priority. As familiarity
with the data increased, the potential benefits of using hyperspectral imaging
became apparent. Today, hyperspectral data are increasingly used for applications ranging from atmospheric characterisation and climate research, snow
and ice hydrology, monitoring of coastal environments, understanding the
structure and functioning of ecosystems, mineral exploration and land use,
land cover and vegetation mapping. The following provides a brief overview
of these applications.
1.6.1
Atmosphere and Hydrosphere
Within the atmospheric sciences, hyperspectral remote sensing has been used
primarily to investigate the retrieval of atmospheric properties, thereby allowing the development and implementation of correction techniques for airborne
and spaceborne remote sensing data (Curran 1994; Green et al. 1998b; Roberts
et al. 1998). In particular, certain regions of the electromagnetic spectrum
are sensitive to different atmospheric constituents. For example, absorption
of water vapour occurs most strongly at 820 nm, 940 nm, 1130 nm, 1380 nm
and 1880 nm (Gao and Goetz 1991) whilst C02 absorption is prominent at
1: Richard Lucas, Aled Rowlands, Olaf Niemann, Ray Merton
Table 1.5. Commercially available software for hyperspectral applications
Software
Environment for Visualizing Images (ENVI)
EASI-PACE
Imagine
Hyperspectral product Generation System
(HPGS)
Hyperspectral Image Processing
and Analysis System (HIPAS)
Spectral Image Processing System (SIPS)
1.6
Applications
Developer/Distributor
Research Systems Inc., USA
PCI Geomatics, Canada
ERDAS, USA
Analytical Imaging and Geophysics, USA
Chinese Academy of Sciences, China
University of Colorado, USA
Although imaging spectrometry has been used in military applications (e. g.,
distinguishing between camouflage and actual vegetation) for many years,
the classified nature of the information has resulted in few published papers
regarding their origins (van der Meer and de Jong 2001). Therefore, as hyperspectral remote sensing data became available to the civilian community,
the early phases of analysis focused largely on understanding the information
content of the data over a disparate range of environments. Subsequently, the
development of algorithms specifically designed to more effectively manipulate the enormous quantities of data generated became a priority. As familiarity
with the data increased, the potential benefits of using hyperspectral imaging
became apparent. Today, hyperspectral data are increasingly used for applications ranging from atmospheric characterisation and climate research, snow
and ice hydrology, monitoring of coastal environments, understanding the
structure and functioning of ecosystems, mineral exploration and land use,
land cover and vegetation mapping. The following provides a brief overview
of these applications.
1.6.1
Atmosphere and Hydrosphere
Within the atmospheric sciences, hyperspectral remote sensing has been used
primarily to investigate the retrieval of atmospheric properties, thereby allowing the development and implementation of correction techniques for airborne
and spaceborne remote sensing data (Curran 1994; Green et al. 1998b; Roberts
et al. 1998). In particular, certain regions of the electromagnetic spectrum
are sensitive to different atmospheric constituents. For example, absorption
of water vapour occurs most strongly at 820 nm, 940 nm, 1130 nm, 1380 nm
and 1880 nm (Gao and Goetz 1991) whilst C02 absorption is prominent at