XXII ISPRS Congress 2012: Technical Commission VII

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/3: INFORMATION EXTRACTION FROM HYPERSPECTRAL DATA]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ROAD CLASSIFICATION AND CONDITION DETERMINATION USING HYPERSPECTRAL IMAGERY M. Mohammadi

Document type:: Multivolume work

Structure type:: Chapter

x 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

Access restriction

Copyright

fullscreen: Technical Commission VII (B7)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation