EMSCNet: Efficient Multisample Contrastive Network for Remote Sensing Image Scene Classification-Reference-Cited by-同舟云学术

EMSCNet: Efficient Multisample Contrastive Network for Remote Sensing Image Scene Classification

Published:2023 Issue: Volume:61 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zhao Yibo¹,Liu Jianjun¹^ORCID,Yang Jinlong¹^ORCID,Wu Zebin²^ORCID

Affiliation:

1. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China

2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

China Postdoctoral Science Foundation

Jiangsu Postdoctoral Research Funding Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10086539.pdf?arnumber=10086539

Reference62 articles.

1. Deep Metric Learning Based on Scalable Neighborhood Components for Remote Sensing Scene Characterization

2. A Multiple-Instance Densely-Connected ConvNet for Aerial Scene Classification

3. Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification

4. Combining Multilevel Features for Remote Sensing Image Scene Classification With Attention Model

5. Momentum Contrast for Unsupervised Visual Representation Learning

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