Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland wat...

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Autores principales:	Gyanendra Prasad Joshi, Fayadh Alenezi, Gopalakrishnan Thirumoorthy, Ashit Kumar Dutta, Jinsang You
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	unmanned aerial vehicles remote sensing deep learning parameter tuning planetscope imagery ensemble model Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/3d9928f159ef440f83a947f0d8e9eb63
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spelling	oai:doaj.org-article:3d9928f159ef440f83a947f0d8e9eb632021-11-25T18:17:49ZEnsemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks10.3390/math92229842227-7390https://doaj.org/article/3d9928f159ef440f83a947f0d8e9eb632021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2984https://doaj.org/toc/2227-7390Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.Gyanendra Prasad JoshiFayadh AleneziGopalakrishnan ThirumoorthyAshit Kumar DuttaJinsang YouMDPI AGarticleunmanned aerial vehiclesremote sensingdeep learningparameter tuningplanetscope imageryensemble modelMathematicsQA1-939ENMathematics, Vol 9, Iss 2984, p 2984 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	unmanned aerial vehicles remote sensing deep learning parameter tuning planetscope imagery ensemble model Mathematics QA1-939
spellingShingle	unmanned aerial vehicles remote sensing deep learning parameter tuning planetscope imagery ensemble model Mathematics QA1-939 Gyanendra Prasad Joshi Fayadh Alenezi Gopalakrishnan Thirumoorthy Ashit Kumar Dutta Jinsang You Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks
description	Recently, unmanned aerial vehicles (UAVs) have been used in several applications of environmental modeling and land use inventories. At the same time, the computer vision-based remote sensing image classification models are needed to monitor the modifications over time such as vegetation, inland water, bare soil or human infrastructure regardless of spectral, spatial, temporal, and radiometric resolutions. In this aspect, this paper proposes an ensemble of DL-based multimodal land cover classification (EDL-MMLCC) models using remote sensing images. The EDL-MMLCC technique aims to classify remote sensing images into the different cloud, shades, and land cover classes. Primarily, median filtering-based preprocessing and data augmentation techniques take place. In addition, an ensemble of DL models, namely VGG-19, Capsule Network (CapsNet), and MobileNet, is used for feature extraction. In addition, the training process of the DL models can be enhanced by the use of hosted cuckoo optimization (HCO) algorithm. Finally, the salp swarm algorithm (SSA) with regularized extreme learning machine (RELM) classifier is applied for land cover classification. The design of the HCO algorithm for hyperparameter optimization and SSA for parameter tuning of the RELM model helps to increase the classification outcome to a maximum level considerably. The proposed EDL-MMLCC technique is tested using an Amazon dataset from the Kaggle repository. The experimental results pointed out the promising performance of the EDL-MMLCC technique over the recent state of art approaches.
format	article
author	Gyanendra Prasad Joshi Fayadh Alenezi Gopalakrishnan Thirumoorthy Ashit Kumar Dutta Jinsang You
author_facet	Gyanendra Prasad Joshi Fayadh Alenezi Gopalakrishnan Thirumoorthy Ashit Kumar Dutta Jinsang You
author_sort	Gyanendra Prasad Joshi
title	Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks
title_short	Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks
title_full	Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks
title_fullStr	Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks
title_full_unstemmed	Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks
title_sort	ensemble of deep learning-based multimodal remote sensing image classification model on unmanned aerial vehicle networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3d9928f159ef440f83a947f0d8e9eb63
work_keys_str_mv	AT gyanendraprasadjoshi ensembleofdeeplearningbasedmultimodalremotesensingimageclassificationmodelonunmannedaerialvehiclenetworks AT fayadhalenezi ensembleofdeeplearningbasedmultimodalremotesensingimageclassificationmodelonunmannedaerialvehiclenetworks AT gopalakrishnanthirumoorthy ensembleofdeeplearningbasedmultimodalremotesensingimageclassificationmodelonunmannedaerialvehiclenetworks AT ashitkumardutta ensembleofdeeplearningbasedmultimodalremotesensingimageclassificationmodelonunmannedaerialvehiclenetworks AT jinsangyou ensembleofdeeplearningbasedmultimodalremotesensingimageclassificationmodelonunmannedaerialvehiclenetworks
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Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks

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