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Roof Type Selection based on patch-based classsification using deep learning for high Resolution Satellite Imagery

Partovi, Tahmineh and Fraundorfer, Friedrich and Azimi, Seyedmajid and Marmanis, Dimitrios and Reinartz, Peter (2017) Roof Type Selection based on patch-based classsification using deep learning for high Resolution Satellite Imagery. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, XLII-1 (W1), pp. 653-657. Copernicus Publications. ISPRS Hannover Workshop: HRIGI 17, 06.-09. Juni 2017, Hannover, Germany. DOI: 10.5194/isprs-archives-XLII-1-W1-653-2017

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Official URL: http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-1-W1/653/2017/

Abstract

3D building reconstruction from remote sensing image data from satellites is still an active research topic and very valuable for 3D city modelling. The roof model is the most important component to reconstruct the Level of Details 2 (LoD2) for a building in 3D modelling. While the general solution for roof modelling relies on the detailed cues (such as lines, corners and planes) extracted from a Digital Surface Model (DSM), the correct detection of the roof type and its modelling can fail due to low quality of the DSM generated by dense stereo matching. To reduce dependencies of roof modelling on DSMs, the pansharpened satellite images as a rich resource of information are used in addition. In this paper, two strategies are employed for roof type classification. In the first one, building roof types are classified in a state-of-the-art supervised pre-trained convolutional neural network (CNN) framework. In the second strategy, deep features from deep layers of different pre-trained CNN model are extracted and then an RBF kernel using SVM is employed to classify the building roof type. Based on roof complexity of the scene, a roof library including seven types of roofs is defined. A new semi-automatic method is proposed to generate training and test patches of each roof type in the library. Using the pre-trained CNN model does not only decrease the computation time for training significantly but also increases the classification accuracy.

Item URL in elib:https://elib.dlr.de/112899/
Document Type:Conference or Workshop Item (Speech)
Title:Roof Type Selection based on patch-based classsification using deep learning for high Resolution Satellite Imagery
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Partovi, Tahminehtahmineh.partovi (at) dlr.deUNSPECIFIED
Fraundorfer, Friedrichfriedrich.fraundorfer (at) dlr.deUNSPECIFIED
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.deUNSPECIFIED
Marmanis, Dimitriosdimitrios.marmanis (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.dehttps://orcid.org/0000-0002-8122-1475
Date:2017
Journal or Publication Title:International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
Volume:XLII-1
DOI :10.5194/isprs-archives-XLII-1-W1-653-2017
Page Range:pp. 653-657
Editors:
EditorsEmail
UNSPECIFIEDISPRS Org.
Publisher:Copernicus Publications
Status:Published
Keywords:Roof Reconstruction, High Resolution Satellite Imagery, Deep Learning Method, Convolutional Neural Networks
Event Title:ISPRS Hannover Workshop: HRIGI 17
Event Location:Hannover, Germany
Event Type:international Conference
Event Dates:06.-09. Juni 2017
Organizer:ISPRS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren, Vorhaben Optical Remote Sensing
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By:INVALID USER
Deposited On:30 Jun 2017 13:31
Last Modified:31 Jul 2019 20:10

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