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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 
CLASSIFICATION OF ROOF MATERIALS USING HYPERSPECTRAL DATA 
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 
ABSTRACT: 
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. 
1. INTRODUCTION AND RELATED RESEARCH 
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.,