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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 
ROAD CLASSIFICATION AND CONDITION DETERMINATION USING 
HYPERSPECTRAL IMAGERY 
M. Mohammadi 
Department of Geomatics, Computer Science and Mathematics, University of Applied Sciences Stuttgart 
SchellingstraBe 24, D-70174 Stuttgart, Germany — mohammadi, 2025( yahoo.com 
KEY WORDS: Hyperspectral, Urban, ALK vector data, Classification, Condition 
ABSTRACT: 
Hyperspectral data has remarkable capabilities for automatic identification and mapping of urban surface materials because of its high 
spectral resolution. It includes a wealth of information which facilitates an understanding of the ground material properties. For 
identification of road surface materials, information about their relation to hyperspectral sensor measurements is needed. In this study an 
approach for classification of road surface materials using hyperspectral data is developed. The condition of the road surface materials, in 
particular asphalt is also investigated. Hyperspectral data with 4m spatial resolution of the city of Ludwigsburg, Germany consisting of 
125 bands (wavelength range of 0.4542um to 2.4846 um) is used. Different supervised classification methods such as spectral angle 
mapper are applied based on a spectral library established from field measurements and in-situ inspection. It is observed that using the 
spectral angle mapper approach with regions of interest is helpful for road surface material identification. Additionally, spectral features 
are tested using their spectral functions in order to achieve better classification results. Spectral functions such as mean and standard 
deviation are suitable for discriminating asphalt, concrete and gravel. Different asphalt conditions (good, intermediate and bad) are 
distinguished using the spectral functions such as mean and image ratio. The mean function gives reliable results. Automatisierte 
Liegenschaftskarte (ALK) vector data for roads is integrated in order to confine the analysis to roads. Reliable reference spectra are useful 
in evaluation of classification results for spectrally similar road surface materials. The classification results are assessed using 
orthophotos and field visits information. 
1. INTRODUCTION AND RELATED RESEARCH 
Comprehensive information about road networks as one of the 
transportation features is helpful for assessment and planning of 
transportation (routing). Retrieval of road information such as 
road surface material and pavement type condition is one of the 
essential issues in urban areas. This is done with either traditional 
surveying or remote sensing (RS) (Zhang and Couloigner, 
2004).The former needs more labour and is more time consuming 
in comparison to the latter. Hyperspectral imagery, also known as 
imaging spectrometry, is the acquisition of data in many narrow, 
contiguous spectral bands (Goetz et al., 1985). It provides more 
detailed information in comparison to other remote sensing 
techniques. Different chemical materials such as asphalt and 
gravel by their corresponding physical (absorption, albedo, 
reflectivity etc) properties can be derived on a very detailed level 
from the hyperspectral imagery. This characteristic is helpful in 
discrimination and extraction of urban area objects especially 
those with similar spectral properties. Road surface materials can 
be identified with hyperspectral imagery with less cost compared 
to field surveying. Most of the available methods for mapping 
roads are either manual or semi-automatic. However, these 
approaches are time consuming and expensive. In particular, that 
they may involve a lot of field work and interpretation of aerial 
imagery from which only 
limited information can be acquired. Hyperspectral data has 
significant potential in terms of automatic identification of road 
surface materials. However, no standard approach for mapping 
road surfaces and identifying the condition of road surface 
materials exists up to date. Most of the methods that exist were 
originally developed for mineral detection. Thus it is a challenge 
to use these methods in identifying road surface materials due to 
the variation of these materials in relatively small regions in the 
case of roads. 
In recent times there has been an increase in the demand to find 
economical automated methods to extract information from 
hyperspectral data due to the fast developments in urban areas. 
Noronha et al, (2002) focus on extraction of road centerlines, 
detecting pavement condition and developing a spectral library. 
The analysis of hyperspectral data is done using MultiSpec 
software and maximum likelihood classification is applied. 
Bhattacharyya distance is used for separability analysis between 
road materials and roof types. For better discrimination of roofs 
and roads, an object-oriented image classification technique is 
used. This technique tries to analyse the homogenous image object 
rather than independent pixels. A multispectral sensor (IKONOS) 
is used as an ancillary sensor. Comparison of configuration for 
urban target separation in spectrometry and multispectral remote 
sensing shows that some of the features are not determined in the 
latter. This is due to the broadness and location of the bands. 
Moreover, its broad band channels do not resolve small-scale 
spectral absorption features which are unique for several built up 
areas. According to the separability analysis results, concrete and