Improved Swin Transformer-Based Semantic Segmentation of Postearthquake Dense Buildings in Urban Areas Using Remote Sensing Images

Improved Swin Transformer-Based Semantic Segmentation of Postearthquake Dense Buildings in Urban Areas Using Remote Sensing Images

Timely acquiring the earthquake-induced damage of buildings is crucial for emergency assessment and post-disaster rescue. Optical remote sensing is a typical method for obtaining seismic data due to its wide coverage and fast response speed. Convolutional neural networks (CNNs) are widely applied fo...

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Bibliographic Details
Main Authors: Liangyi Cui, Xin Jing, Yu Wang, Yixuan Huan, Yang Xu, Qiangqiang Zhang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Attention mechanism
complex weather disturbances
dense seismic building segmentation
feature fusion
improved Swin Transformer
remote sensing images
Online Access:https://ieeexplore.ieee.org/document/9964408/

Internet

https://ieeexplore.ieee.org/document/9964408/

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