Remote sensing scene classification based on high-order graph convolutional network-Reference-Cited by-同舟云学术

Remote sensing scene classification based on high-order graph convolutional network

Published:2021-01-04 Issue:sup1 Volume:54 Page:141-155
ISSN:2279-7254
Container-title:European Journal of Remote Sensing
language:en
Short-container-title:European Journal of Remote Sensing

Author:

Gao Yue¹,Shi Jun²^ORCID,Li Jun²,Wang Ruoyu²

Affiliation:

1. Department of Information Engineering, Space Star Technology Co., Ltd, Beijing, China

2. School of Software, Hefei University of Technology, Hefei, China

Funder

Fundamental Research Funds for the Central Universities 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/10.1080/22797254.2020.1868273

Reference38 articles.

1. Abu-El-Haija, S., Alipourfard, N., Harutyunyan, H., Kapoor, A. & Perozzi, B. (2018). A higher-order graph convolutional layer. In 32nd Conference on Neural Information Processing Systems (NIPS), Montréal, Canada. http://sami.haija.org/papers/high-order-gc-layer.pdf.

2. Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Steeg, G. V. & Galstyan, A. (2019). MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. arXiv preprint, arXiv: 1905.00067.

3. APDC-Net: Attention Pooling-Based Convolutional Network for Aerial Scene Classification

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