Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to sel...

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Bibliographic Details
Main Authors: Raju Anand, Sathishkumar Samiaappan, Shanmugham Veni, Ethan Worch, Meilun Zhou
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Journal of Imaging
Subjects:
moth–flame
optimization
hyperspectral image
classifier
particle swarm
genetic algorithm
Online Access:https://www.mdpi.com/2313-433X/8/5/126

Internet

https://www.mdpi.com/2313-433X/8/5/126

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