Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks

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

Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

Abstract

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%.

Originalspracheenglisch
Aufsatznummer8486983
Seiten (von - bis)1100-1116
Seitenumfang17
FachzeitschriftIEEE transactions on geoscience and remote sensing
Jahrgang57
Ausgabenummer2
DOIs
PublikationsstatusVeröffentlicht - Feb 2019

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Synthetic aperture radar
Convolution
synthetic aperture radar
Neural networks
Image classification
image classification
computer vision
TerraSAR-X
Radar imaging
detection
Recurrent neural networks
footprint
Computer vision
tomography
train
Tomography
Masks
Remote sensing
pixel
Pixels

Schlagwörter

    ASJC Scopus subject areas

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

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    Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks. / Shahzad, Muhammad; Maurer, Michael; Fraundorfer, Friedrich; Wang, Yuanyuan; Zhu, Xiao Xiang.

    in: IEEE transactions on geoscience and remote sensing, Jahrgang 57, Nr. 2, 8486983, 02.2019, S. 1100-1116.

    Publikation: Beitrag in einer FachzeitschriftArtikelForschungBegutachtung

    Shahzad, Muhammad ; Maurer, Michael ; Fraundorfer, Friedrich ; Wang, Yuanyuan ; Zhu, Xiao Xiang. / Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks. in: IEEE transactions on geoscience and remote sensing. 2019 ; Jahrgang 57, Nr. 2. S. 1100-1116.
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