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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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Autores principales: Anand, Raju, Samiaappan, Sathishkumar, Veni, Shanmugham, Worch, Ethan, Zhou, Meilun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
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
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author Anand, Raju
Samiaappan, Sathishkumar
Veni, Shanmugham
Worch, Ethan
Zhou, Meilun
author_facet Anand, Raju
Samiaappan, Sathishkumar
Veni, Shanmugham
Worch, Ethan
Zhou, Meilun
author_sort Anand, Raju
collection PubMed
description 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 select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon’s distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets—Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy.
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spelling pubmed-91443462022-05-29 Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization Anand, Raju Samiaappan, Sathishkumar Veni, Shanmugham Worch, Ethan Zhou, Meilun J Imaging Article 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 select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon’s distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets—Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy. MDPI 2022-04-26 /pmc/articles/PMC9144346/ /pubmed/35621891 http://dx.doi.org/10.3390/jimaging8050126 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Anand, Raju
Samiaappan, Sathishkumar
Veni, Shanmugham
Worch, Ethan
Zhou, Meilun
Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization
title Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization
title_full Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization
title_fullStr Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization
title_full_unstemmed Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization
title_short Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization
title_sort airborne hyperspectral imagery for band selection using moth–flame metaheuristic optimization
topic Article
url 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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