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

 
: Ruf, Boitumelo; Thiel, Laurenz; Weinmann, Martin

:
Fulltext urn:nbn:de:0011-n-5828433 (15 MByte PDF)
MD5 Fingerprint: 818068daa386b8fee7c04a2e252ea08e
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Created on: 27.3.2020


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 Annals IV-1)
pp.125-132
Mid-term Symposium "Innovative Sensing - From Sensors to Methods and Applications" <2018, Karlsruhe>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
aerial oblique imagery; object detection; building extraction; deep learning; Convolutional Neural Networks; transfer learning; depth estimation; semi-global matching

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.

: http://publica.fraunhofer.de/documents/N-582843.html