Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm

Satellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combina...

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Autores principales:	Kowsalya Thiagarajan, Mukunthan Manapakkam Anandan, Andrzej Stateczny, Parameshachari Bidare Divakarachari, Hemalatha Kivudujogappa Lingappa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	accuracy Convolutional Neural Networks Correlation Coefficient-Based Gravitational Search Algorithm ensemble learning hierarchical framework satellite image classification Science Q
Acceso en línea:	https://doaj.org/article/0732d090664346cbb940767f8622237c
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spelling	oai:doaj.org-article:0732d090664346cbb940767f8622237c2021-11-11T18:54:30ZSatellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm10.3390/rs132143512072-4292https://doaj.org/article/0732d090664346cbb940767f8622237c2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4351https://doaj.org/toc/2072-4292Satellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combination of Hierarchical Framework and Ensemble Learning (HFEL) and optimal feature selection is proposed for the precise identification of satellite images. The HFEL uses three different types of Convolutional Neural Networks (CNN), namely AlexNet, LeNet-5 and a residual network (ResNet), to extract the appropriate features from images of the hierarchical framework. Additionally, the optimal features from the feature set are extracted using the Correlation Coefficient-Based Gravitational Search Algorithm (CCGSA). Further, the Multi Support Vector Machine (MSVM) is used to classify the satellite images by extracted features from the fully connected layers of the CNN and selected features of the CCGSA. Hence, the combination of HFEL and CCGSA is used to obtain the precise classification over different datasets such as the SAT-4, SAT-6 and Eurosat datasets. The performance of the proposed HFEL–CCGSA is analyzed in terms of accuracy, precision and recall. The experimental results show that the HFEL–CCGSA method provides effective classification over the satellite images. The classification accuracy of the HFEL–CCGSA method is 99.99%, which is high when compared to AlexNet, LeNet-5 and ResNet.Kowsalya ThiagarajanMukunthan Manapakkam AnandanAndrzej StatecznyParameshachari Bidare DivakarachariHemalatha Kivudujogappa LingappaMDPI AGarticleaccuracyConvolutional Neural NetworksCorrelation Coefficient-Based Gravitational Search Algorithmensemble learninghierarchical frameworksatellite image classificationScienceQENRemote Sensing, Vol 13, Iss 4351, p 4351 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	accuracy Convolutional Neural Networks Correlation Coefficient-Based Gravitational Search Algorithm ensemble learning hierarchical framework satellite image classification Science Q
spellingShingle	accuracy Convolutional Neural Networks Correlation Coefficient-Based Gravitational Search Algorithm ensemble learning hierarchical framework satellite image classification Science Q Kowsalya Thiagarajan Mukunthan Manapakkam Anandan Andrzej Stateczny Parameshachari Bidare Divakarachari Hemalatha Kivudujogappa Lingappa Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
description	Satellite image classification is widely used in various real-time applications, such as the military, geospatial surveys, surveillance and environmental monitoring. Therefore, the effective classification of satellite images is required to improve classification accuracy. In this paper, the combination of Hierarchical Framework and Ensemble Learning (HFEL) and optimal feature selection is proposed for the precise identification of satellite images. The HFEL uses three different types of Convolutional Neural Networks (CNN), namely AlexNet, LeNet-5 and a residual network (ResNet), to extract the appropriate features from images of the hierarchical framework. Additionally, the optimal features from the feature set are extracted using the Correlation Coefficient-Based Gravitational Search Algorithm (CCGSA). Further, the Multi Support Vector Machine (MSVM) is used to classify the satellite images by extracted features from the fully connected layers of the CNN and selected features of the CCGSA. Hence, the combination of HFEL and CCGSA is used to obtain the precise classification over different datasets such as the SAT-4, SAT-6 and Eurosat datasets. The performance of the proposed HFEL–CCGSA is analyzed in terms of accuracy, precision and recall. The experimental results show that the HFEL–CCGSA method provides effective classification over the satellite images. The classification accuracy of the HFEL–CCGSA method is 99.99%, which is high when compared to AlexNet, LeNet-5 and ResNet.
format	article
author	Kowsalya Thiagarajan Mukunthan Manapakkam Anandan Andrzej Stateczny Parameshachari Bidare Divakarachari Hemalatha Kivudujogappa Lingappa
author_facet	Kowsalya Thiagarajan Mukunthan Manapakkam Anandan Andrzej Stateczny Parameshachari Bidare Divakarachari Hemalatha Kivudujogappa Lingappa
author_sort	Kowsalya Thiagarajan
title	Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
title_short	Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
title_full	Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
title_fullStr	Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
title_full_unstemmed	Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm
title_sort	satellite image classification using a hierarchical ensemble learning and correlation coefficient-based gravitational search algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0732d090664346cbb940767f8622237c
work_keys_str_mv	AT kowsalyathiagarajan satelliteimageclassificationusingahierarchicalensemblelearningandcorrelationcoefficientbasedgravitationalsearchalgorithm AT mukunthanmanapakkamanandan satelliteimageclassificationusingahierarchicalensemblelearningandcorrelationcoefficientbasedgravitationalsearchalgorithm AT andrzejstateczny satelliteimageclassificationusingahierarchicalensemblelearningandcorrelationcoefficientbasedgravitationalsearchalgorithm AT parameshacharibidaredivakarachari satelliteimageclassificationusingahierarchicalensemblelearningandcorrelationcoefficientbasedgravitationalsearchalgorithm AT hemalathakivudujogappalingappa satelliteimageclassificationusingahierarchicalensemblelearningandcorrelationcoefficientbasedgravitationalsearchalgorithm
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Satellite Image Classification Using a Hierarchical Ensemble Learning and Correlation Coefficient-Based Gravitational Search Algorithm

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