Improving damage classification via hybrid deep learning feature representations derived from post-earthquake aerial images-Reference-Cited by-同舟云学术

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Improving damage classification via hybrid deep learning feature representations derived from post-earthquake aerial images

Published:2020-12-30 Issue:1 Volume:13 Page:1-20
ISSN:1947-9832
Container-title:International Journal of Image and Data Fusion
language:en
Short-container-title:International Journal of Image and Data Fusion

Author:

Settou Tarablesse¹²,Kholladi Mohamed-Khireddine¹³^ORCID,Ben Ali Abdelkamel¹^ORCID

Affiliation:

1. Department of Computer Science, University of El Oued, El Oued, Algeria

2. Laboratory of Operator Theory and EDP: Foundations and Application, University of El Oued, Algeria

3. MISC Laboratory of Constantine 2, University of Constantine 2, Algeria

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications

Link

https://www.tandfonline.com/doi/pdf/10.1080/19479832.2020.1864787

Reference43 articles.

1. Analysis of satellite images for disaster detection

2. Object-Based Building Damage Assessment Methodology Using Only Post Event ALOS-2/PALSAR-2 Dual Polarimetric SAR Intensity Images

3. A Convolutional Neural Network Approach for Assisting Avalanche Search and Rescue Operations with UAV Imagery

4. Rapid Earthquake Damage Detection Using Deep Learning from VHR Remote Sensing Images

5. Bianchi, F.M. et al. 2019. Snow avalanche segmentation in SAR images with fully convolutional neural networks. arXiv preprint arXiv:1910.05411.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Scene-level buildings damage recognition based on Cross Conv-Transformer;International Journal of Digital Earth;2023-09-28

2. A Novel Noise-Aware Deep Learning Model for Underwater Acoustic Denoising;IEEE Transactions on Geoscience and Remote Sensing;2023

3. MSBDA-Net: Multi-scale Siamese Building Damage Assessment Network;2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE);2022-12-18

4. Deep learning for seismic structural monitoring by accounting for mechanics-based model uncertainty;Journal of Building Engineering;2022-10

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