Optimizing the Hyperparameters of Fully Convolutional Encoder–Decoder Networks for SAR Image Segmentation-Reference-Cited by-同舟云学术

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Optimizing the Hyperparameters of Fully Convolutional Encoder–Decoder Networks for SAR Image Segmentation

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Liu Yuanyue¹^ORCID,Zhao Jin¹^ORCID,Fan Jianchao²^ORCID,Wang Jun³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, China

2. School of Control Science and Engineering, Dalian University of Technology, Dalian, China

3. Department of Computer Science and School of Data Science, City University of Hong Kong, Hong Kong

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

National High Resolution Special Research

Fundamental Research Funds for the Central Universities

Foundation of Intelligent Ecotourism Subject Group of Chongqing Three Gorges University

Chongqing Graduate Student Research Innovation Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/8859/10365397/10604908.pdf?arnumber=10604908

Reference24 articles.

1. Fully Convolutional Networks for Semantic Segmentation

2. Floating raft aquaculture information automatic extraction based on high resolution SAR images

3. U-Net: Convolutional Networks for Biomedical Image Segmentation

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

5. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

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