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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Detalles Bibliográficos
Autores principales: Anand, Raju, Samiaappan, Sathishkumar, Veni, Shanmugham, Worch, Ethan, Zhou, Meilun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144346/
https://www.ncbi.nlm.nih.gov/pubmed/35621891
http://dx.doi.org/10.3390/jimaging8050126

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9144346/
https://www.ncbi.nlm.nih.gov/pubmed/35621891
http://dx.doi.org/10.3390/jimaging8050126

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