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Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road netw...

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Autores principales: Khan, Mohd Jawed, Singh, Pankaj Pratap, Pradhan, Biswajeet, Alamri, Abdullah, Lee, Chang-Wook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649272/
https://www.ncbi.nlm.nih.gov/pubmed/37960482
http://dx.doi.org/10.3390/s23218783
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author Khan, Mohd Jawed
Singh, Pankaj Pratap
Pradhan, Biswajeet
Alamri, Abdullah
Lee, Chang-Wook
author_facet Khan, Mohd Jawed
Singh, Pankaj Pratap
Pradhan, Biswajeet
Alamri, Abdullah
Lee, Chang-Wook
author_sort Khan, Mohd Jawed
collection PubMed
description Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network’s ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.
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spelling pubmed-106492722023-10-28 Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network Khan, Mohd Jawed Singh, Pankaj Pratap Pradhan, Biswajeet Alamri, Abdullah Lee, Chang-Wook Sensors (Basel) Article Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network’s ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods. MDPI 2023-10-28 /pmc/articles/PMC10649272/ /pubmed/37960482 http://dx.doi.org/10.3390/s23218783 Text en © 2023 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
Khan, Mohd Jawed
Singh, Pankaj Pratap
Pradhan, Biswajeet
Alamri, Abdullah
Lee, Chang-Wook
Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
title Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
title_full Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
title_fullStr Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
title_full_unstemmed Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
title_short Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network
title_sort extraction of roads using the archimedes tuning process with the quantum dilated convolutional neural network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649272/
https://www.ncbi.nlm.nih.gov/pubmed/37960482
http://dx.doi.org/10.3390/s23218783
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