Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation-Reference-Cited by-同舟云学术

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Hi-ResNet: Edge Detail Enhancement for High-Resolution Remote Sensing Segmentation

Published:2024 Issue: Volume:17 Page:15024-15040
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Chen Yuxia¹^ORCID,Fang Pengcheng²,Zhong Xiaoling¹^ORCID,Yu Jianhui³,Zhang Xiaoming⁴^ORCID,Li Tianrui⁴^ORCID

Affiliation:

1. School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu, China

2. University of Southampton, Southampton, U.K.

3. School of Computer Science, University of Sydney, Sydney, NSW, Australia

4. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10638169.pdf?arnumber=10638169

Reference100 articles.

1. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks

2. Building Extraction From RGB VHR Images Using Shifted Shadow Algorithm

3. Multilayer Feature Extraction Network for Military Ship Detection From High-Resolution Optical Remote Sensing Images

4. Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake

5. Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network

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