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Classification and representation of commonly used roofing materials using multisensorial aerial data

 
: Ilehag, R.; Bulatov, Dimitri; Helmholtz, P.; Belton, D.

:
Volltext urn:nbn:de:0011-n-5197497 (4.2 MByte PDF)
MD5 Fingerprint: c19c205ee0da162678c821c48b0119bc
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Erstellt am: 29.11.2018


Jutzi, B. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
ISPRS TC I Mid-term Symposium "Innovative Sensing - From Sensors to Methods and Applications" 2018 : 10-12 October 2018, Karlsruhe, Germany
Istanbul: ISPRS, 2018 (ISPRS Archives XLII-1)
S.217-224
Mid-term Symposium "Innovative Sensing - From Sensors to Methods and Applications" <2018, Karlsruhe>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
multispectral; high-resolution RGB; building outlines

Abstract
As more cities are starting to experience the urban heat islands effect, knowledge about the energy emitted from building roofs is of primary importance. Since this energy depends both on roof orientations and materials, we tackled both issues by analysing sensor data from multispectral, thermal infrared, high-resolution RGB, and airborne laser datasets (each with different spatial resolutions) of a council in Perth, Australia. To localise the roofs, we acquired building outlines that had to be updated using the normalised digital surface model, the NDVI and the planarity. Then, we computed a semantic 3D model of the study area, with roof detail analysis being a particular focus. The main objective of this study, however, was to classify three commonly used roofing materials: Cement tiles, Colorbond and Zincalume by combining the multispectral and thermal infrared image bands while the high-resolution RGB dataset was used to provide additional information about the roof texture. Three types of image segmentation approaches were evaluated to assess any differences while performing the material classification; pixel-wise, superpixel-wise and building-wise image segmentation. Due to the limited amount of labelled data, we extended the dataset by labelling data ourselves and merged Colorbond and Zincalume into one separate class. The supervised classifier Random Forest was applied to all reasonable configurations of segmentation kinds, numbers of classes, and finally, keeping track of the added value of principal component analysis.

: http://publica.fraunhofer.de/dokumente/N-519749.html