Graph-Based Semi-Supervised Learning With Tensor Embeddings for Hyperspectral Data Classification-Reference-Cited by-同舟云学术

Graph-Based Semi-Supervised Learning With Tensor Embeddings for Hyperspectral Data Classification

Published:2023 Issue: Volume:11 Page:124819-124832
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Georgoulas Ioannis¹^ORCID,Protopapadakis Eftychios²^ORCID,Makantasis Konstantinos³^ORCID,Seychell Dylan³^ORCID,Doulamis Anastasios¹^ORCID,Doulamis Nikolaos¹^ORCID

Affiliation:

1. School of Rural and Surveying Engineering, National Technical University of Athens, Athens, Greece

2. School of Information Sciences, University of Macedonia, Thessaloniki, Greece

3. Department of Artificial Intelligence, University of Malta, Msida, Malta

Funder

H2020 Industrial Leadership

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10301611.pdf?arnumber=10301611

Reference38 articles.

1. Tensor-based embedding for graph-based semi-supervised approaches

2. Semi-supervised hyperspectral image classification algorithm based on graph embedding and discriminative spatial information

3. Multiclass Data Segmentation Using Diffuse Interface Methods on Graphs

4. Multireceptive field: An adaptive path aggregation graph neural framework for hyperspectral image classification

5. A Constrained Graph-Based Semi-Supervised Algorithm Combined with Particle Cooperation and Competition for Hyperspectral Image Classification

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