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

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. 2. STUDY AREA, HYPERSPECTRAL IMAGE DATA AND PREPROCESSING 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. 3. METHODS AND RESULTS 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 Background 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 anal; opti of tl to | desc disc: feat in tl used avai the clas: map shov The an | geoi whi prot repr indi clos colc repr for trail Def prol prol het: TOO: trai vari for

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