Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India

Climate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. An important factor in climate change is the change in the...

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Autores principales:	Madhu Khurana, Vikas Saxena
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Change detection Extreme learning machine Adaptive ensemble of extreme learning machines Green cover NDVI Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/24bc244b8143481eb404706cf795cdf0
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spelling	oai:doaj.org-article:24bc244b8143481eb404706cf795cdf02021-11-22T04:19:35ZGreen cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India1319-157810.1016/j.jksuci.2018.09.008https://doaj.org/article/24bc244b8143481eb404706cf795cdf02021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1319157818303665https://doaj.org/toc/1319-1578Climate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. An important factor in climate change is the change in the green cover. Non-availability of standard datasets and limited labeled data points makes it difficult to attain high accuracies in change detection. In this paper, we have proposed a modified ensemble of extreme learning machines (mAEELM) for change detection in green cover to increase the accuracy of the change detection process. It uses Extreme Learning Machine (ELM) as the base classifier. Different ELMs are trained with different configuration so that they have different learning capabilities. These ELMs are combined to create an ensemble and it is then adapted based on the accuracy of the individual ELMs. The ensemble is then pruned to eliminate the ELMs which are not contributing towards the overall result of the ensemble, to make it more efficient. The proposed algorithm has been applied for detecting change on two areas of Gandhuan, Punjab and Chaparkaura Kham, Uttar Pradesh, India. The algorithm shows an average accuracy of 97.8% on both the datasets.Madhu KhuranaVikas SaxenaElsevierarticleChange detectionExtreme learning machineAdaptive ensemble of extreme learning machinesGreen coverNDVIElectronic computers. Computer scienceQA75.5-76.95ENJournal of King Saud University: Computer and Information Sciences, Vol 33, Iss 10, Pp 1265-1273 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Change detection Extreme learning machine Adaptive ensemble of extreme learning machines Green cover NDVI Electronic computers. Computer science QA75.5-76.95
spellingShingle	Change detection Extreme learning machine Adaptive ensemble of extreme learning machines Green cover NDVI Electronic computers. Computer science QA75.5-76.95 Madhu Khurana Vikas Saxena Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India
description	Climate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. An important factor in climate change is the change in the green cover. Non-availability of standard datasets and limited labeled data points makes it difficult to attain high accuracies in change detection. In this paper, we have proposed a modified ensemble of extreme learning machines (mAEELM) for change detection in green cover to increase the accuracy of the change detection process. It uses Extreme Learning Machine (ELM) as the base classifier. Different ELMs are trained with different configuration so that they have different learning capabilities. These ELMs are combined to create an ensemble and it is then adapted based on the accuracy of the individual ELMs. The ensemble is then pruned to eliminate the ELMs which are not contributing towards the overall result of the ensemble, to make it more efficient. The proposed algorithm has been applied for detecting change on two areas of Gandhuan, Punjab and Chaparkaura Kham, Uttar Pradesh, India. The algorithm shows an average accuracy of 97.8% on both the datasets.
format	article
author	Madhu Khurana Vikas Saxena
author_facet	Madhu Khurana Vikas Saxena
author_sort	Madhu Khurana
title	Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India
title_short	Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India
title_full	Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India
title_fullStr	Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India
title_full_unstemmed	Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India
title_sort	green cover change detection using a modified adaptive ensemble of extreme learning machines for north-western india
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/24bc244b8143481eb404706cf795cdf0
work_keys_str_mv	AT madhukhurana greencoverchangedetectionusingamodifiedadaptiveensembleofextremelearningmachinesfornorthwesternindia AT vikassaxena greencoverchangedetectionusingamodifiedadaptiveensembleofextremelearningmachinesfornorthwesternindia
_version_	1718418211660103680

Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India

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