Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images-Reference-Cited by-同舟云学术

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Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images

Published:2021 Issue: Volume:14 Page:2284-2294
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 Ziyi^ORCID,Wang Cheng^ORCID,Li Jonathan^ORCID,Xie Nianci^ORCID,Han Yan^ORCID,Du Jixiang

Funder

National Natural Science Foundation of China

Natural Science Foundation of Fujian Province

United National Natural Science Foundation of China

Special National Key Research and Development Plan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9314330/09333652.pdf?arnumber=9333652

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1. Remote sensing image road network detection based on channel attention mechanism;Heliyon;2024-09

2. AM-UNet: Road Network Extraction from high-resolution Aerial Imagery Using Attention-Based Convolutional Neural Network;Journal of the Indian Society of Remote Sensing;2024-08-21

3. A new optimization approach based on neural architecture search to enhance deep U-Net for efficient road segmentation;Knowledge-Based Systems;2024-07

4. A Review of Deep Learning-Based Methods for Road Extraction from High-Resolution Remote Sensing Images;Remote Sensing;2024-06-07

5. DPIF-Net: a dual path network for rural road extraction based on the fusion of global and local information;PeerJ Computer Science;2024-05-31

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