FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification-Reference-Cited by-同舟云学术

FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification

Published:2022 Issue: Volume:60 Page:1-13
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Jin Pu¹,Mou Lichao²^ORCID,Hua Yuansheng²^ORCID,Xia Gui-Song³^ORCID,Zhu Xiao Xiang²^ORCID

Affiliation:

1. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China

2. German Aerospace Center, Remote Sensing Technology Institute, Weßling, Germany

3. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) and the School of Computer Science, Wuhan University, Wuhan, China

Funder

Bundesministerium fr Bildung und Forschung

H2020 European Research Council

HelmholtzGemeinschaft

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09709809.pdf?arnumber=9709809

Reference68 articles.

1. Long Short-Term Memory

2. Video Classification With Channel-Separated Convolutional Networks

3. Convolutional Learning of Spatio-temporal Features

4. 3D Convolutional Neural Networks for Human Action Recognition

5. Learning Spatiotemporal Features with 3D Convolutional Networks

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