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International Journal of Remote Sensing Volume 38, 2017 - Issue 4
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Articles

Detecting damaged building parts in earthquake-damaged areas using differential seeded region growing

Junho YeomEngineering Research Institute, Seoul National University, Seoul, Republic of KoreaORCID Iconhttp://orcid.org/0000-0001-7571-1155View further author information
ORCID Icon,
Minyoung JungDepartment of Civil and Environmental Engineering, Seoul National University, Seoul, Republic of KoreaView further author information
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Yongil KimDepartment of Civil and Environmental Engineering, Seoul National University, Seoul, Republic of KoreaCorrespondence[email protected]
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Pages 985-1005 | Received 29 Mar 2016, Accepted 08 Dec 2016, Published online: 13 Jan 2017

Citations (9)

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Read on this site (3)

Ghasem Abdi & Shabnam Jabari. (2021) A Multi-Feature Fusion Using Deep Transfer Learning for Earthquake Building Damage Detection. Canadian Journal of Remote Sensing 47:2, pages 337-352.
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Hamed Amini Amirkolaee & Hossein Arefi. (2019) CNN-based estimation of pre- and post-earthquake height models from single optical images for identification of collapsed buildings. Remote Sensing Letters 10:7, pages 679-688.
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Zhihua Xu, Lixin Wu & Zhenxin Zhang. (2018) Use of active learning for earthquake damage mapping from UAV photogrammetric point clouds. International Journal of Remote Sensing 39:15-16, pages 5568-5595.
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Articles from other publishers (6)

Hang Zhang, Jian Liu, Zhaochuan Hu, Ning Chen, Zhiyuan Yang & Junhua Shen. (2022) Design of a deep learning visual system for the thickness measurement of each coating layer of TRISO-coated fuel particles. Measurement 191, pages 110806.
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Ghasem AbdiMorteza EsfandiariShabnam Jabari. (2021) A deep transfer learning-based damage assessment on post-event very high-resolution orthophotos. Geomatica 75:4, pages 237-250.
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Ghasem Abdi, Morteza Esfandiari & Shabnam Jabari. (2021) Building Damage Detection in Post-Event High-Resolution Imagery Using Deep Transfer Learning. Building Damage Detection in Post-Event High-Resolution Imagery Using Deep Transfer Learning.
Chaoxian Liu, Haigang Sui & Lihong Huang. (2021) Identification of Damaged Building Regions From High- Resolution Images Using Superpixel-Based Gradient and Autocorrelation Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, pages 1010-1024.
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Mürsel Ozan İNCETAŞ & Ufuk TANYERİ. (2019) THE EFFECTS OF NOISE FILTERS ON SEGMENTATION BASED SEEDED REGION GROWINGGÜRÜLTÜ FİLTRELERİNİN TOHUMLU ALAN GENİŞLETME TABANLI BÖLÜTLEMEYE ETKİLERİ. Mühendislik Bilimleri ve Tasarım Dergisi 7:4, pages 725-735.
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Junho Yeom, Youkyung Han, Anjin Chang & Jinha Jung. (2019) Hurricane Building Damage Assessment using Post-Disaster UAV Data. Hurricane Building Damage Assessment using Post-Disaster UAV Data.

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