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  4. Deep Cross-Domain Building Extraction for Selective Depth Estimation from Oblique Aerial Imagery
 
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2018
Conference Paper
Titel

Deep Cross-Domain Building Extraction for Selective Depth Estimation from Oblique Aerial Imagery

Abstract
With the technological advancements of aerial imagery and accurate 3d reconstruction of urban environments, more and more attention has been paid to the automated analyses of urban areas. In our work, we examine two important aspects that allow online analysis of building structures in city models given oblique aerial image sequences, namely automatic building extraction with convolutional neural networks (CNNs) and selective real-time depth estimation from aerial imagery. We use transfer learning to train the Faster R-CNN method for real-time deep object detection, by combining a large ground-based dataset for urban scene understanding with a smaller number of images from an aerial dataset. We achieve an average precision (AP) of about 80% for the task of building extraction on a selected evaluation dataset. Our evaluation focuses on both dataset-specific learning and transfer learning. Furthermore, we present an algorithm that allows for multi-view depth estimation from aerial image sequences in real-time. We adopt the semi-global matching (SGM) optimization strategy to preserve sharp edges at object boundaries. In combination with the Faster R-CNN, it allows a selective reconstruction of buildings, identified with regions of interest (RoIs), from oblique aerial imagery.
Author(s)
Ruf, Boitumelo
Thiel, Laurenz
Weinmann, Martin
Hauptwerk
ISPRS TC I Mid-term Symposium "Innovative Sensing - From Sensors to Methods and Applications" 2018
Konferenz
Mid-term Symposium "Innovative Sensing - From Sensors to Methods and Applications" 2018
DOI
10.5194/isprs-annals-IV-1-125-2018
File(s)
N-582843.pdf (15.94 MB)
Language
English
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Tags
  • aerial oblique imager...

  • object detection

  • building extraction

  • deep learning

  • Convolutional Neural ...

  • transfer learning

  • depth estimation

  • semi-global matching

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