Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery-Reference-Cited by-同舟云学术

Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery

Published:2019-08-20 Issue:3 Volume:41 Page:1040-1066
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Song Dongmei¹²³^ORCID,Tan Xuan¹⁴^ORCID,Wang Bin¹²³^ORCID,Zhang Ling⁵,Shan Xinjian³,Cui Jianyong¹

Affiliation:

1. The School of Geosciences, China University of Petroleum (East China), Qingdao, PR China

2. The Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, PR China

3. The State Key Laboratory of Earthquake Dynamics, Beijing, PR China

4. The Graduate School of the China University of Petroleum, Qingdao, PR China

5. The National Earthquake Response Support Service, Beijing, China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2019.1655175

Reference65 articles.

1. Structural Damage Assessments from Ikonos Data Using Change Detection, Object-Oriented Segmentation, and Classification Techniques

2. Building-damage detection using post-seismic high-resolution SAR satellite data

3. Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery

4. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

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