You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Technical Commission VII


International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia
C. Chisense
Department of Geomatics, Computer Science and Mathematics, University of Applied Sciences Stuttgart
SchellingstraBe 24, D-70174 Stuttgart (Germany), - chembe.chisense@yahoo.com
KEY WORDS: Hyperspectral, Urban, ALK Vector data, Classification, Feature extraction, Roof
Mapping of surface materials in urban areas using aerial imagery is a challenging task. This is because there are numerous materials
present in relatively small regions. Hyperspectral data features a fine spectral resolution and thus has a significant capability for
automatic identification and mapping of urban surface materials. In this study an approach for identification of roof surface materials
using hyperspectral data is presented. The study is based on an urban area in Ludwigsburg, Germany, using a HyMap data set recorded
during the HyMap campaign in August, 2010. Automatisierte Liegenschaftskarte (ALK) vector data with a building layer is combined
with the HyMap data to limit the analysis to roofs. A spectral library for roofs is compiled based on field and image measurements. In the
roof material identification process, supervised classification methods, namely spectral angle mapper and spectral information divergence
and the object oriented ECHO (extraction and classification of homogeneous objects) approach are compared. In addition to the overall
shape of spectral curves, position and strength of absorptions features are used to enhance material identification. The discriminant
analysis feature extraction method is applied to the HyMap data in order to identify features (band combinations) suitable for
discriminating between the target classes. The identified optimal features are used to create a new data set which is later classified using
the ECHO classifier. The classification results with respect to material types of roofs are presented in this study. The most important
results are evaluated using orthophotos, probability maps and field visits.
Urban environments are characterized by many different artificial
and natural surface materials which reflect and influence
ecological, climatic and energetic conditions of cities. They
include mixtures of materials ranging from concrete, wood, tiles,
bitumen, metal, sand and stone. Complete inventories based on
analog mapping are very expensive and time consuming.
Hyperspectral data has a high spectral resolution. Therefore, it has
a high potential for material oriented mapping of urban surfaces
and enables the recognition of characteristic features of urban
surface materials. Thus it can be expected that surface materials
can be detected on a very detailed level from the hyperspectral
imagery. However, the development of optimal methods for
analysing hyperspectral data is still a challenge. So far there is no
standard approach to material classification. Problems are the
high within-class variability of many materials and the presence
of numerous materials in relatively small regions. A hyperspectral
pixel in an urban scene features most frequently a mixture of
different material components; the classification therefore
requires a decomposition of the corresponding spectral signature
into its “pure” constituents (Bhaskaran and Datt, 2000).
Most of the research done on hyperspectral data in the past
focused on mineral detection rather than urban surface materials
such as roofs. This has changed recently due to the high pace of
city development and the increase in the need to find efficient
methods for mapping urban surface cover types. (Roessner et al.,
2001) develop an automated method for hyperspectral image
analysis exploiting the spectral and spatial information content of
data in order to differentiate urban surface cover types. To
achieve this, a hierarchical structure of categories is developed.
The main categories are defined as sealed (buildings, roads etc.)
and non-sealed surfaces (vegetation, bare soil). Similar research is
carried out by (Segl et al., 2003). They analyse urban surfaces
taking into account their spectral and shape characteristics in the
reflective and thermal wavelength range. A new algorithm for an
improved detection of pure pixels is incorporated in an approach
developed for automated identification of urban surface cover
types, which combines spectral classification and unmixing
techniques to facilitate sensible endmember detection.
(Dell'Acqua et al., 2004) investigate spatial reclassification and
mathematical morphology approaches. Spectral and spatial
classifiers are combined in a multiclassification framework. The
use of morphological approaches gives high overall accuracies.
The approach taken by (Powell et al., 2007) is similar to that
adopted in the present study. They build a regionally specific
(Manaus, Brazil) spectral library of urban materials based on
generalized categories of urban land cover components such as
vegetation and impervious surfaces. Almost 97% of the image
pixels are modeled within 2.5% RMS error constraint. The RMS
error indicates the overall fit of the linear unmixing. (Heiden et
al., 2007) propose a new approach for the determination and
evaluation of spectral features that are robust against spectral
overlap between material classes and within-class variability. The
approach is divided into two parts. In the first part, spectral
features for each material of interest are defined that allow an
optimal identification and separation based on the reference
spectra contained in the spectral library. For the second part, the
robustness of these features is evaluated by a separability
analysis. The results show that urban materials need to be
described by more than one type of feature. Materials
characterized by distinct absorption bands and/or reflectance
peaks can be well detected using functions such as ratio, area,
absorption depth and position. Additionally, the idea of
integrating ancillary data in the analysis used by (Heldens et al.,