XXII ISPRS Congress 2012: Technical Commission VII

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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:: CLASSIFICATION OF ROOF MATERIALS USING HYPERSPECTRAL DATA C. Chisense

Document type:: Multivolume work

Structure type:: Chapter

ts 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.,

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