A Global-to-Local Evolutionary Algorithm for Hyperspectral Endmember Extraction-Reference-Cited by-同舟云学术

A Global-to-Local Evolutionary Algorithm for Hyperspectral Endmember Extraction

Published:2023 Issue: Volume:61 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Cheng Fan¹^ORCID,Chen Naikun²,Wang Chao¹^ORCID,Wang Qijun¹^ORCID,Du Bo³^ORCID

Affiliation:

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

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

3. 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/ielx7/36/10006360/10036465.pdf?arnumber=10036465

Reference61 articles.

1. A Novel Endmember Bundle Extraction and Clustering Approach for Capturing Spectral Variability Within Endmember Classes

2. Robust Multiobjective Optimization for Vehicle Routing Problem With Time Windows

3. EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing

4. A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows

5. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

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