TY - GEN
T1 - BFGAN-building footprint extraction from satellite images
AU - Shi, Yilei
AU - Li, Qingyu
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this work, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN with a cost function derived from the Wasserstein distance and added a gradient penalty term. The achieved results indicated that the proposed method can significantly improve the quality of building footprint generation compared to conditional generative adversarial networks, the U-Net, and other networks. In addition, our method nearly removes all hyperparameter tuning.
AB - Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this work, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN with a cost function derived from the Wasserstein distance and added a gradient penalty term. The achieved results indicated that the proposed method can significantly improve the quality of building footprint generation compared to conditional generative adversarial networks, the U-Net, and other networks. In addition, our method nearly removes all hyperparameter tuning.
KW - Wasserstein generative adversarial networks (WGANs)
KW - building footprint
KW - conditional generative adversarial networks (CGANs)
KW - generative adversarial networks (GANs)
UR - http://www.scopus.com/inward/record.url?scp=85072051590&partnerID=8YFLogxK
U2 - 10.1109/JURSE.2019.8809048
DO - 10.1109/JURSE.2019.8809048
M3 - Conference contribution
AN - SCOPUS:85072051590
T3 - 2019 Joint Urban Remote Sensing Event, JURSE 2019
BT - 2019 Joint Urban Remote Sensing Event, JURSE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 Joint Urban Remote Sensing Event, JURSE 2019
Y2 - 22 May 2019 through 24 May 2019
ER -