Image Processing Reference
containing much more surface features such as grass, asphalt, concrete, roof shingles,
and colored metal surfaces from cars. It is then possible to either leave it to the
software to define appropriate endmember models for each pixel or to define
possible combinations in advance and only chose among those.
Imaging spectroscopy is at present a tool largely driven by technological improve-
ments. In the near future, advanced spectrometers will emerge that will open up the
road to new analysis tools and new ways to employ them. Such sensors will enhance
our ability to differentiate materials or to model quantitative indicators from primary
parameters like surface reflectance. One of these near future developments is the
Airborne Reflective and Emissive imaging Spectrometer (ARES) with 155 spectral
bands including the thermal infrared and an excellent SNR (Wilson and Cocks
2003 ). Also, spaceborne high resolution spectrometers with satisfactory SNR will
become available in a few years, such as the Environmental Mapping and Analysis
Program (EnMAP, Buckingham and Staenz 2008 ).
Moreover, the combination of hyperspectral with other remote sensing data and
enhanced analysis techniques offers a high potential of further improvements in data
analysis. Sensor integration may include data fusion concepts between very high
geometric resolution and hyperspectral data (Lehmann et al. 1998 ). Such sensor
combinations are particularly valuable for urban applications as an improved geo-
metric resolution will result in less mixed pixel surfaces. From a processing point-of-
view, combined analysis schemes such as the integration of supervised classification
and spectral unmixing (Segl et al. 2000 ) or the use of machine learning classifiers
(van der Linden et al. 2007 ) offer new opportunities, especially in the heterogeneous
urban environment. Finally, it has to be remarked that quantitative analyses and
modeling approaches will become more relevant in the future. While there are
examples of quantitative models of soil or vegetation properties (e.g. Schlerf et al.
2005 ) such approaches have not yet been implemented for urban applications.
Hyperspectral remote sensing data differ from multispectral data in the num-
ber of spectral bands and hence in the analysis options associated with such
data. These extended analysis opportunities are on one hand particularly use-
ful in a heterogeneous urban environment. On the other hand, this heterogene-
ity results in demanding pre-processing schemes and accuracy level that need
to be achieved. Radiometric pre-processing focuses on illumination and atmo-
spheric corrections. As all hyperspectral data used for urban applications are
acquired by airborne sensors nowadays, the geometric pre-processing includ-
ing DGPS and INS information is demanding.