An improved method for rapid un-collapsed building extraction from post-disaster high-resolution remote sensing imagery based on multi-scale feature alignment-Reference-Cited by-同舟云学术

An improved method for rapid un-collapsed building extraction from post-disaster high-resolution remote sensing imagery based on multi-scale feature alignment

Published:2024-04-26 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Chen Meng¹,Wu Jianjun¹²³,Mao Ting¹,Du Ruohua¹,Zhao Bingyu¹,Lin Jingyu¹,Zhang Jianhang¹

Affiliation:

1. Faculty of Geographical Science, Beijing Normal University, Beijing, People’s Republic of China

2. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing, People’s Republic of China

3. Beijing Key Laboratory for Remote Sensing of Environmental and Digital Cities, Beijing, People’s Republic of China

Funder

National Key Research and Development Project of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2344599

Reference46 articles.

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2. Class-Aware Domain Adaptation for Coastal Land Cover Mapping Using Optical Remote Sensing Imagery

3. Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation Using Region and Category Adaptive Domain Discriminator

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

5. Chen Liang-Chieh George Papandreou Florian Schroff and Hartwig Adam. 2017. “Rethinking Atrous Convolution for Semantic Image Segmentation.” arXiv:1706.05587.

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