Image Processing Reference
In-Depth Information
(e.g. Ben-Dor 2001 ; Herold et al. 2004 ). From a remote sensing point-of-view,
the urban setting differs from natural or semi-natural environments due to a few
distinct characteristics:
Object heterogeneity or texture: Many urban features exhibit sharp borderlines,
while their inner-object variance may vary substantially. A large parking lot with
cars may appear extremely heterogeneous, while the neighboring industrial
complex is represented by a few homogeneous roof constructions.
Landscape heterogeneity and object size: Object size and heterogeneity are often
interlinked. It is also sometimes difficult to specify average object sizes for a
complex environment such as the city. However, the size of most objects (houses,
cars, street width) may be regarded as relatively small (Small 2003 ), compared to
other situations (agricultural fields, forest plots, open water surfaces). The
amount of mixed pixels resulting from this circumstance varies, depending on the
pixel size, but is usually much higher than in most other cases.
Combination of natural and anthropogenic materials: Urban surfaces include a
great variety of spectrally distinct surfaces. Urban areas may well include large
areas consisting of natural materials (vegetation, soils, water), as “urban” is not
necessarily defined through the built environment. Theoretically, mixtures of all
natural and anthropogenic materials may occur.
Geometric complexity: The application of airborne sensors and the associated
wide field-of-view angles (compare 9.2) results in extreme differences in
object illumination. A sensor records the shaded backside of built-up areas with
scan angles opposite to the sun azimuth, scan angles parallel to the sun azimuth
lead to a view on illuminated facades. The strength of this effect varies with sun
elevation/azimuth, object geometry/spectral behavior, and flight direction, i.e. it
is a spectrally varying function depending on sun-object-sensor geometry.
Details and explanations on the spectral and geometric behavior of urban surfaces
are given elsewhere in this volume. However, even from this short introduction it
becomes apparent that the analysis of such an environment provides an enormous
challenge for remote sensing based data analysis, monitoring approaches, and the-
matic assessments. One of the options to tackle object and landscape heterogeneity
is to employ high spectral resolution remote sensing data.
There is no precise definition of which number of bands separates multispectral
from hyperspectral data. One may, for example, agree that sensors allowing for a
detailed analysis of absorption features in their spectral
range fall in the category of hyperspectral data. To date,
there is no operational hyperspectral satellite sensor
offering an adequate geometric resolution for urban
applications. With the advent of the Airborne Visible /
Infrared Imaging Spectrometer (AVIRIS) in 1987, the
first airborne hyperspectral imager with 224 contiguous
spectral bands between 400 and 2,500 nm was available
for a wide range of applications. Sensors like the Digital
Airborne Imaging Spectrometer (DAIS 7915) featuring
Hyperspectral data
differ from
multispectral data
in the number of
bands and band
widths. Spectral
signatures from
hyperspectral data
appear contiguous
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