Sparse Unmixing for Hyperspectral Imagery via Comprehensive-Learning-Based Particle Swarm Optimization

Sparse Unmixing for Hyperspectral Imagery via Comprehensive-Learning-Based Particle Swarm Optimization

Sparse unmixing methods have been extensively studied as a popular topic in hyperspectral image analysis for several years. Fundamental model-based unmixing problems can be better reformulated by exploiting sparse constraints in different forms. Gradient-based optimization approaches commonly serve...

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Bibliographic Details
Main Authors: Yapeng Miao, Bin Yang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Comprehensive learning
double swarms
hyperspectral imagery
particle swarm optimization
solution space partition
sparse regularization
Online Access:https://ieeexplore.ieee.org/document/9547738/

Internet

https://ieeexplore.ieee.org/document/9547738/

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