A Saliency-Aware Deep Network for Narrow Road Extraction of High-Resolution Remote Sensing Imagery-Reference-Cited by-同舟云学术

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A Saliency-Aware Deep Network for Narrow Road Extraction of High-Resolution Remote Sensing Imagery

Published:2024-07-07 Issue: Volume: Page:8450-8453
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Wang Ningjing¹,Pan Yang²,Wang Xinyu³,Yao Wanqiang¹,Zhong Yanfei²

Affiliation:

1. Xi'an University of Science and Technology,College of Geomatics,China

2. Wuhan University,State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing,China

3. Wuhan University,School of Remote Sensing and Information Engineering,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642899.pdf?arnumber=10642899

Reference8 articles.

1. E2EVAP: End-to-end vectorization of smallholder agricultural parcel boundaries from high-resolution remote sensing imagery

2. Mapping the distribution of invasive tree species using deep one-class classification in the tropical montane landscape of Kenya

3. ImageNet classification with deep convolutional neural networks

4. GAMSNet: Globally aware road detection network with multi-scale residual learning

5. D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

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