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 |
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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 | |
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