Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks

Muhammad Shahzad, Michael Maurer, Friedrich Fraundorfer, Yuanyuan Wang, Xiao Xiang Zhu

Research output: Contribution to journalArticlepeer-review


This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high-resolution (VHR) synthetic aperture radar (SAR) images. In this context, this paper has two major contributions. First, it presents a novel and generic workflow that initially classifies the spaceborne SAR tomography (TomoSAR) point clouds-generated by processing VHR SAR image stacks using advanced interferometric techniques known as TomoSAR-into buildings and nonbuildings with the aid of auxiliary information (i.e., either using openly available 2-D building footprints or adopting an optical image classification scheme) and later back project the extracted building points onto the SAR imaging coordinates to produce automatic large-scale benchmark labeled (buildings/nonbuildings) SAR data sets. Second, these labeled data sets (i.e., building masks) have been utilized to construct and train the state-of-the-art deep fully convolution neural networks with an additional conditional random field represented as a recurrent neural network to detect building regions in a single VHR SAR image. Such a cascaded formation has been successfully employed in computer vision and remote sensing fields for optical image classification but, to our knowledge, has not been applied to SAR images. The results of the building detection are illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering approximately 39 km 2 - A lmost the whole city of Berlin-with the mean pixel accuracies of around 93.84%.

Original languageEnglish
Article number8486983
Pages (from-to)1100-1116
Number of pages17
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number2
Publication statusPublished - Feb 2019


  • Building detection
  • Buildings
  • Feature extraction
  • fully convolution neural networks (CNNs)
  • OpenStreetMap (OSM)
  • Optical distortion
  • Optical imaging
  • Optical interferometry
  • Optical sensors
  • SAR tomography (TomoSAR)
  • Synthetic aperture radar
  • synthetic aperture radar (SAR)
  • TerraSAR-X/TanDEM-X.
  • TerraSAR-X/TanDEM-X

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)


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