Extraction of buildings in vhr sar images using fully convolution neural networks

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-SkyMed, can deliver very high resolution (VHR) data beyond the inherent spatial scales (on the order of 1m) of buildings, constituting invaluable data source for large-scale urban mapping. Processing this VHR data with advanced interferometric techniques, such as SAR tomography (TomoSAR), enables the generation of 3-D (or even 4-D) TomoSAR point clouds from space. In this paper, we present a novel and generic workflow that exploits these TomoSAR point clouds in a way that is capable to automatically produce benchmark annotated (buildings/nonbuildings) SAR datasets. These annotated datasets (building masks) have been utilized to construct and train the state-ofthe- A rt 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. The results of building detection are illustrated and validated over TerraSAR-X VHR spotlight SAR image covering approximately 39 km2 . almost the whole city of Berlin . with mean pixel accuracies of around 93.84%.

Originalspracheenglisch
Titel2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
Seiten4367-4370
Seitenumfang4
ISBN (elektronisch)9781538671504
DOIs
PublikationsstatusVeröffentlicht - 31 Okt 2018
Veranstaltung38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spanien
Dauer: 22 Jul 201827 Jul 2018

Publikationsreihe

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Band2018-July

Konferenz

Konferenz38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
LandSpanien
OrtValencia
Zeitraum22/07/1827/07/18

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    ASJC Scopus subject areas

    • !!Computer Science Applications
    • !!Earth and Planetary Sciences(all)

    Dieses zitieren

    Shahzad, M., Maurer, M., Fraundorfer, F., Wang, Y., & Zhu, X. X. (2018). Extraction of buildings in vhr sar images using fully convolution neural networks. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (S. 4367-4370). [8519603] (International Geoscience and Remote Sensing Symposium (IGARSS); Band 2018-July). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IGARSS.2018.8519603