Swarm intelligence optimization-based spectral unmixing

Lianru Gao; Xu Sun; Zhu Han; Lina Zhuang; Wenfei Luo; Bing Zhang

doi:10.1002/9781119687788.ch15

Swarm intelligence optimization-based spectral unmixing

Lianru Gao^*, Xu Sun, Zhu Han, Lina Zhuang, Wenfei Luo, Bing Zhang

^*Corresponding author for this work

Department of Mathematics

Research output: Chapter in book/report/conference proceeding › Chapter › peer-review

1 Citation (Scopus)

Abstract

Due to the relatively low spatial resolution of sensors and the complex distributions of materials, many mixed pixels exist in the hyperspectral imagery and inevitably degrade the performance of high-level data processing. Swarm intelligence (SI) algorithm is one of major techniques to solve some difficult optimization problems and has been successfully used in the application of hyperspectral unmixing. This chapter is particularly interested in SI algorithms that can be implemented in three spectral mixing models: linear mixing model (LMM), normal compositional model (NCM), and nonlinear mixing model (NLMM).

Original language	English
Title of host publication	Advances in Hyperspectral Image Processing Techniques
Editors	Chein-I Chang
Publisher	Wiley Blackwell
Chapter	15
Pages	422-452
Number of pages	31
ISBN (Electronic)	9781119687757, 9781119687771
ISBN (Print)	9781119687788, 9781119687764
DOIs	https://doi.org/10.1002/9781119687788.ch15
Publication status	Published - 16 Nov 2022

Scopus Subject Areas

General Engineering

User-Defined Keywords

Hyperspectral unmixing
Linear mixing model
Non-linear mixing model
Normal compositional model
Particle swarm optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1002/9781119687788.ch15

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title = "Swarm intelligence optimization-based spectral unmixing",

abstract = "Due to the relatively low spatial resolution of sensors and the complex distributions of materials, many mixed pixels exist in the hyperspectral imagery and inevitably degrade the performance of high-level data processing. Swarm intelligence (SI) algorithm is one of major techniques to solve some difficult optimization problems and has been successfully used in the application of hyperspectral unmixing. This chapter is particularly interested in SI algorithms that can be implemented in three spectral mixing models: linear mixing model (LMM), normal compositional model (NCM), and nonlinear mixing model (NLMM).",

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author = "Lianru Gao and Xu Sun and Zhu Han and Lina Zhuang and Wenfei Luo and Bing Zhang",

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year = "2022",

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doi = "10.1002/9781119687788.ch15",

language = "English",

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T1 - Swarm intelligence optimization-based spectral unmixing

AU - Gao, Lianru

AU - Sun, Xu

AU - Han, Zhu

AU - Zhuang, Lina

AU - Luo, Wenfei

AU - Zhang, Bing

PY - 2022/11/16

Y1 - 2022/11/16

N2 - Due to the relatively low spatial resolution of sensors and the complex distributions of materials, many mixed pixels exist in the hyperspectral imagery and inevitably degrade the performance of high-level data processing. Swarm intelligence (SI) algorithm is one of major techniques to solve some difficult optimization problems and has been successfully used in the application of hyperspectral unmixing. This chapter is particularly interested in SI algorithms that can be implemented in three spectral mixing models: linear mixing model (LMM), normal compositional model (NCM), and nonlinear mixing model (NLMM).

AB - Due to the relatively low spatial resolution of sensors and the complex distributions of materials, many mixed pixels exist in the hyperspectral imagery and inevitably degrade the performance of high-level data processing. Swarm intelligence (SI) algorithm is one of major techniques to solve some difficult optimization problems and has been successfully used in the application of hyperspectral unmixing. This chapter is particularly interested in SI algorithms that can be implemented in three spectral mixing models: linear mixing model (LMM), normal compositional model (NCM), and nonlinear mixing model (NLMM).

KW - Hyperspectral unmixing

KW - Linear mixing model

KW - Non-linear mixing model

KW - Normal compositional model

KW - Particle swarm optimization

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M3 - Chapter

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SN - 9781119687764

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BT - Advances in Hyperspectral Image Processing Techniques

A2 - Chang, Chein-I

PB - Wiley Blackwell

ER -

Swarm intelligence optimization-based spectral unmixing

Abstract

Scopus Subject Areas

User-Defined Keywords

UN SDGs

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