TY - GEN
T1 - Extraction of buildings in vhr sar images using fully convolution neural networks
AU - Shahzad, Muhammad
AU - Maurer, Michael
AU - Fraundorfer, Friedrich
AU - Wang, Yuanyuan
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - 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%.
AB - 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%.
KW - Building Detection
KW - Fully Convolution Neural Networks
KW - OpenStreetMap
KW - SAR Tomography
KW - Synthetic Aperture Radar (SAR)
KW - TerraSAR-X/TanDEM-X
UR - https://www.scopus.com/pages/publications/85060662337
U2 - 10.1109/IGARSS.2018.8519603
DO - 10.1109/IGARSS.2018.8519603
M3 - Conference contribution
AN - SCOPUS:85060662337
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4367
EP - 4370
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -