Image Processing Reference
bands to be rectified. Considering urban environments, the precise co-registration
with cadastral data or similarly high resolution geometries is particularly demanding.
The advantage may be that precise reference data often exist for urban environments,
which is not necessarily the case for other settings.
In an ideal case, airborne data are provided as an image cube accompanied by an
auxiliary data stream of differential global positioning system positions (DGPS) and
inertial navigation system data (INS). The first provides sub-meter accurate position
data of the sensor during image acquisition (x-, y-, and z-coordinates), the latter
information on roll, pitch, and yaw movements of the platform (k-, j-, and w-angles).
Assuming a correct synchronization between scan lines and auxiliary data, it is pos-
sible to calculate the acquisition geometry for every pixel. A DEM has to be included
to correct for terrain induced distortions (Schläpfer and Richter 2002 ).
It will usually be necessary to incorporate ground control points (GCPs) in this
processing scenario to correct for inaccuracies in the measurements itself and for
potential erroneous synchronization between data and auxiliary data. This is a rather
straightforward task in the case of urban environments, as either ground-based DGPS
measurements, orthophotos, or accurate vector data are available or may be retrieved
(in the case of DGPS measurements) for many urban areas. The diversity and crisp-
ness of urban features supports the identification of accurate GCPs. Additionally,
accurate ground truth allows for a high-quality assessment of geometrically
corrected data sets.
One of the most advantageous conceptual frameworks in hyperspectral remote
sensing is based on the opportunity to relate field- or laboratory based spectrometric
measurements with imaging spectrometry data from air-
borne or spaceborne sensors. The spectral behavior of
distinct objects on the Earth's surface is determined by
their physical and chemical properties. While a few work-
ing groups have started to collect such spectra, the avail-
able databases are far from exhaustive (ASTER 1998 ;
Ben-Dor 2001 ; Heiden et al. 2001 ; Hostert and Damm
2003 ). Recently, a structured approach to acquiring a more complete urban spectral
library and to analyze material separability has been exemplified for the Santa
Barbara region by Herold et al. ( 2004 ) and is illustrated in this textbook.
Measurements of the respective components under controlled conditions in the
laboratory or under real-world conditions in the field can hence be related to the
surface's physical or chemical properties (quantitative approaches); alternatively,
such measurements may serve as well-defined samples to identify similar components
(qualitative approaches) in imaging spectrometry data. The ability to relate radio-
metrically corrected hyperspectral data from diverse sensors with ground-based
spectroradiometric data can be regarded as a spectral upscaling.