Fanet: A deep learning framework for black and odorous water extraction-Reference-Cited by-同舟云学术

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Fanet: A deep learning framework for black and odorous water extraction

Published:2023-07-14 Issue:1 Volume:56 Page:
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Zheng Guizhou¹^ORCID,Zhao Yingying¹^ORCID,Pan Zixuan²,Chen Zhixing¹,Qiu Zhonghang³,Zheng Tingting⁴

Affiliation:

1. School of Geography and Information Engineering, China University of Geosciences, Wuhan, China

2. Henan Water Conservancy Survey., LTD, Zhengzhou, China

3. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

4. School of Resources and Environment, Shanxi Agricultural University, Jinzhong, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2023.2234077

Reference57 articles.

1. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

2. A critical review of the appearance of black-odorous waterbodies in China and treatment methods

3. Global Context-Aware Progressive Aggregation Network for Salient Object Detection

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multiscale Feature Fusion and Threshold-Based Attentional YOLO for Tailings Ponds Detection of Remote Sensing Images;IEEE Geoscience and Remote Sensing Letters;2024

2. Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model;Sustainability;2023-12-21

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