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Technical Commission VII

2008) is also adopted in this research. The goal of the research by
(Heldens et al., 2008) is to identify urban surface materials in
Munich from HyMap data. They use an unmixing approach to
identify surface materials. The unmixing approach is performed
with and without a building mask. The quality of the co-
registration of the hyperspectral data and the building mask has a
large impact on the results.
For a more comprehensive review of related work please refer to
Chisense (2011).
In the present study, a material classification is performed for
roofs only. The corresponding areas are identified by means of
ALK vector data; hereby the confusion between roofs and roads
with similar spectral properties is avoided. Information is
extracted from the hyperspectral data using the “Discriminant
Analysis Feature Extraction” method (DAFE). This method is
used to determine an optimal set of features for further analysis,
where in this particular case the term “feature” means “spectral
band”. Those linear combinations of bands are considered as
optimal, for which the ratio of between-class variability and
within-class variability is a maximum, see e.g. (Kuo and
Landgrebe, 2001). The just stated variational problem leads to a
generalized eigenvalue problem. A subset of transformed bands is
selected depending on their corresponding eigenvalues. The new
data set is classified using a spatial-spectral classifier (object-
based) known as “Extraction and Classification of Homogenous
Objects” (ECHO). This method provides for a good
discrimination of spectrally similar materials belonging to
spatially different objects. Orthophotos, classification probability
maps and field visits are used in order to evaluate the
classification results. For the classification of roofs using
hyperspectral data various methods have been investigated, but
only those which give the most successful experimental results
are discussed.
The hyperspectral data used in this study was acquired by German
Aerospace Center (DLR) on 20th August, 2010 in the course of
its annual HyMap campaign. The data covers the city of
Ludwigsburg, Germany, which is located close to Stuttgart in the
Neckar basin. The scene comprises six strips and extends also
over adjacent rural areas, apart from Ludwigsburg city itself. The
total area amounts to 11 km x 16 km. The data include a variety
of typical urban structures such as residential and industrial zones,
railway stations and different roads. We restricted our tests to a
smaller area of approximately 2.0 x 1.1 km2. The data consists of
125 bands (ranging from 0.4 pm to 2.5 um) and has a ground
sample distance of 4 m. ALK vector data with a building layer is
provided by Fachbereich Stadtplanung und Vermessung der Stadt
Ludwigsburg. This vector data is used for limiting the analysis to
roofs. The preprocessing of the hyperspectral data was done by
DLR; in particular the data was corrected for radiometric,
geometric and atmospheric effects. A high resolution LiDAR
surface model of Ludwigsburg with 2 m raster size was made
available for this purpose. An overlay of the HyMap and vector
data reveals a shift between the two data sets in the order of 10 m.
The vector layer for roofs is selected as the source of ground
control points. The GCPs are used to georeference the HyMap

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
data. It is observed that an overall RMS error of about 0.7 pixels
is obtained after carrying out an affine transformation.
In order to determine a suitable approach, the roof material
investigation is carried out in the test area before extending to the
whole research area.
3.1 Roof surface material identification
A first look in a shop for a dealer of roofing materials already
points out that a great variety of roof surface materials has to be
faced in roof surface material identification. Roof tiles made from
materials like clay and slate are on the market as well as those
made from concrete and plastic. The widely used clay tiles are
manufactured again in different ways for instance some clay tiles
have waterproof glaze.
In the initial stage of the research work, 10 materials are selected
for roof material identification. Three materials, namely bitumen,
red roof chipping and zinc plated sheet out of the ten are
identified by name from previous field visits. The remaining
seven are assigned arbitrary names for identification purposes and
include roof material for Kaufland shopping centre
(Ludwigsburg), roof material 1, 2, 3, 4, 5 and 6. In order to map
the distribution of the ten roof materials in the scene, the
discriminant analysis feature extraction (DAFE) available in
MultiSpec Software is applied and this is followed by
classification of the data set created from the optimal features
(band combinations) resulting from the feature extraction.
MultiSpec is a data analysis software intended for analysis of
multispectral image data or hyperspectral data. The following
steps are used in order to accomplish the analysis tasks:
Selection of classes and their training sets: In order to identify
and define suitable training regions, the Hymap data is classified
using an unsupervised classification algorithm. The ISODATA
algorithm is used for this purpose. With the aid of the output map,
training regions for the 10 classes of material are defined. A class
for vegetation (eleventh class) is added after it is observed that
certain building parts include vegetation. Additionally, a class for
the masked out area (background) is also defined.
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Cluster 8
3 Cluster 9
Cluster 10

Figure 1: Output map of unsupervised classification process.
Feature extraction and classification: After designating a set of
training regions, a class conditional preprocessing algorithm
based on a method known as projection pursuit is performed. This
algorithm does the necessary calculations in projected space
rather than the original, high dimensional space thus reducing the
dimensionality of the data. This is followed by the discriminant

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