Unsupervised Hyperspectral Band Selection via Structure-Conserved and Neighborhood-Grouped Evolutionary Algorithm-Reference-Cited by-同舟云学术

Unsupervised Hyperspectral Band Selection via Structure-Conserved and Neighborhood-Grouped Evolutionary Algorithm

Published:2023 Issue: Volume:61 Page:1-19
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Wang Qijun¹^ORCID,Song Chaoping²,Dong Yanni³^ORCID,Cheng Fan¹^ORCID,Tong Lyuyang⁴,Du Bo⁴^ORCID,Zhang Xingyi¹^ORCID

Affiliation:

1. Information Materials and Intelligent Sensing Laboratory of Anhui Province, the Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, and the School of Artificial Intelligence, Anhui University, Hefei, China

2. School of Computer Science and Technology, Anhui University, Hefei, China

3. School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China

4. School of Computer Science, Wuhan University, Wuhan, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

Key Program of Natural Science Project of Educational Commission of Anhui Province

University Synergy Innovation Program of Anhui Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10236472.pdf?arnumber=10236472

Reference50 articles.

1. Local-View-Assisted Discriminative Band Selection With Hypergraph Autolearning for Hyperspectral Image Classification

2. Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection

3. Clustering-Based Hyperspectral Band Selection Using Information Measures

4. A Fast Neighborhood Grouping Method for Hyperspectral Band Selection

5. Unsupervised hyperspectral band selection using ranking based on a denoising error matching approach

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