Spectral–Spatial and Superpixelwise Unsupervised Linear Discriminant Analysis for Feature Extraction and Classification of Hyperspectral Images-Reference-Cited by-同舟云学术

Spectral–Spatial and Superpixelwise Unsupervised Linear Discriminant Analysis for Feature Extraction and Classification of Hyperspectral Images

Published:2023 Issue: Volume:61 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Lu Pengyu¹^ORCID,Jiang Xinwei¹^ORCID,Zhang Yongshan¹^ORCID,Liu Xiaobo²^ORCID,Cai Zhihua¹^ORCID,Jiang Junjun³^ORCID,Plaza Antonio⁴^ORCID

Affiliation:

1. School of Computer Science and the Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, China

2. School of Automation and the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences, Wuhan, China

3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

4. Department of Technology of Computers and Communications, Hyperspectral Computing Laboratory, Escuela Politecnica, University of Extremadura, Caceres, Spain

Funder

Natural Science Foundation of Hubei Province

National Natural Science Foundation of China

Fundamental Research Funds for National University, China University of Geosciences, Wuhan, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10309991.pdf?arnumber=10309991

Reference31 articles.

1. Semisupervised Sparse Manifold Discriminative Analysis for Feature Extraction of Hyperspectral Images