Image Processing Reference
In-Depth Information
hyperspectral thermal infrared capabilities offer additional prospects for urban
analysis; however, thermal infrared devices for narrow band sensors offer a critical
signal-to-noise ratio and a stable calibration appears difficult. For this chapter,
examples from field or laboratory measurements, sampled with an ASD FieldSpec
Pro II spectroradiometer (Hostert and Damm 2003 ), and a subset of HyMap data
acquired in July 2003 over Berlin, Germany, (DLR 2003 ) are given.
An adequate pre-processing of hyperspectral data is a
mandatory prerequisite to extract useful information
from hyperspectral data, regardless of working in urban
or other environments. However, the analysis of urban
properties must be regarded among the most demanding
applications in terms of hyperspectral image pre-processing.
This applies on one hand to the requirements for a pre-
cise co-registration with other raster or vector data sets
(van der Linden and Hostert 2009). On the other hand, the
spectral variability and complex illumination geometry ask for a precise definition
of radiometric correction processes. It is therefore not surprising that pre-processing
of hyperspectral data consists of a not to be underestimated series of processing
steps, in terms of complexity as well as in terms of the amount of effort and time.
From the end-user point of view, pre-processing of remote sensing data can be
divided into preliminary quality assessment, correction of bidirectional effects, geo-
metric correction, and radiometric correction. A screening for spatial, spectral or
radiometric errors should be performed to detect problematic regions of an image.
Usually, bands with particularly low signal-to-noise-ratio (SNR) are discarded. Such
a screening may also include further steps such as cloud and cloud shadow mapping
or the definition of areas with uncharacteristic directional reflectance behavior (e.g.
regions of specular reflectance in the case of water targets).
Most airborne scanners are characterized by a wide field-of-view (FOV) resulting
in different directional reflectance behavior of similar targets depending on sun-
surface-sensor geometry. As hyperspectral data are almost exclusively acquired
with airborne sensors today, correcting for wavelength dependent bidirectional
effects is obligatory for most analyses (Schiefer et al. 2006 ). This can be achieved
by calculating and individually applying a view angle dependent and band-wise
polynomial function in across-track direction, also referred to as “across-track illu-
mination correction”. Mean column-wise reflectance values are calculated for each
spectral band and differences interpreted as the scan-angle dependent variations in
reflectance. It is obvious that such a simplistic approach does not account for land
cover dependant differences in bidirectional behavior. As the urban environment is
spatially extremely heterogeneous, a pre-classification in dominating land cover
classes allows for a class-wise calculation and correction of directional properties.
Hyperspectral data
require a dedicated
This is particularly
true in the case
of urban
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