Tanmay Kumar Behera , Pankaj Kumar Sa , Michele Nappi , Sambit Bakshi
{"title":"基于卫星物联网的超像素cnn结构VHR图像道路提取","authors":"Tanmay Kumar Behera , Pankaj Kumar Sa , Michele Nappi , Sambit Bakshi","doi":"10.1016/j.bdr.2022.100334","DOIUrl":null,"url":null,"abstract":"<div><p><span>In the past few decades, technology has progressively become ineluctable in human lives, primarily due to the growth of certain fields like space technology, Big Data, the Internet of Things<span><span> (IoT), and machine learning. Space technology has revolutionized communication mechanisms while creating opportunities for various research areas, including remote sensing (RS)-inspired applications. On the other hand, IoT presents a platform to use the power of the internet over a whole range of devices through a phenomenon known as social IoT. These devices generate a humongous amount of data that requires handling and managing by big data technology incorporated with </span>deep learning techniques<span><span> to reduce the manual workload of an operator. Moreover, deep learning architectures like </span>convolutional neural networks<span><span> (CNNs) have presented a scope to extract the underlying features from the large-scale input images in providing better solutions for tasks such as automatic road detection that come at the cost of time and memory overhead. In this context, we have proposed a three-layer edge-fog-cloud-based intelligent satellite IoT architecture that uses the superpixel-based CNN approach. At the fog layer, the superpixel-based simple linear iterative cluster (SLIC) algorithm uses the images captured by the satellites of the edge level to produce the smaller-sized </span>superpixel<span> images that can be transferred even in a low bandwidth link. The CNN module at the cloud level is then trained with these superpixel images to predict the road networks from these </span></span></span></span></span>RS images. Two popular road datasets: the DeepGlobe Road dataset and the Massachusetts Road dataset, have been considered to prove the usefulness of the proposed SLIC-CNN architecture in satellite-based IoT platforms to address the problems like RS image-based road extraction. The proposed architecture achieves better performance accuracy than the classical CNN while reducing the incurred overhead by a noticeable limit.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"30 ","pages":"Article 100334"},"PeriodicalIF":3.5000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Satellite IoT Based Road Extraction from VHR Images Through Superpixel-CNN Architecture\",\"authors\":\"Tanmay Kumar Behera , Pankaj Kumar Sa , Michele Nappi , Sambit Bakshi\",\"doi\":\"10.1016/j.bdr.2022.100334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>In the past few decades, technology has progressively become ineluctable in human lives, primarily due to the growth of certain fields like space technology, Big Data, the Internet of Things<span><span> (IoT), and machine learning. Space technology has revolutionized communication mechanisms while creating opportunities for various research areas, including remote sensing (RS)-inspired applications. On the other hand, IoT presents a platform to use the power of the internet over a whole range of devices through a phenomenon known as social IoT. These devices generate a humongous amount of data that requires handling and managing by big data technology incorporated with </span>deep learning techniques<span><span> to reduce the manual workload of an operator. Moreover, deep learning architectures like </span>convolutional neural networks<span><span> (CNNs) have presented a scope to extract the underlying features from the large-scale input images in providing better solutions for tasks such as automatic road detection that come at the cost of time and memory overhead. In this context, we have proposed a three-layer edge-fog-cloud-based intelligent satellite IoT architecture that uses the superpixel-based CNN approach. At the fog layer, the superpixel-based simple linear iterative cluster (SLIC) algorithm uses the images captured by the satellites of the edge level to produce the smaller-sized </span>superpixel<span> images that can be transferred even in a low bandwidth link. The CNN module at the cloud level is then trained with these superpixel images to predict the road networks from these </span></span></span></span></span>RS images. Two popular road datasets: the DeepGlobe Road dataset and the Massachusetts Road dataset, have been considered to prove the usefulness of the proposed SLIC-CNN architecture in satellite-based IoT platforms to address the problems like RS image-based road extraction. The proposed architecture achieves better performance accuracy than the classical CNN while reducing the incurred overhead by a noticeable limit.</p></div>\",\"PeriodicalId\":56017,\"journal\":{\"name\":\"Big Data Research\",\"volume\":\"30 \",\"pages\":\"Article 100334\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2022-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Big Data Research\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214579622000284\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Research","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214579622000284","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Satellite IoT Based Road Extraction from VHR Images Through Superpixel-CNN Architecture
In the past few decades, technology has progressively become ineluctable in human lives, primarily due to the growth of certain fields like space technology, Big Data, the Internet of Things (IoT), and machine learning. Space technology has revolutionized communication mechanisms while creating opportunities for various research areas, including remote sensing (RS)-inspired applications. On the other hand, IoT presents a platform to use the power of the internet over a whole range of devices through a phenomenon known as social IoT. These devices generate a humongous amount of data that requires handling and managing by big data technology incorporated with deep learning techniques to reduce the manual workload of an operator. Moreover, deep learning architectures like convolutional neural networks (CNNs) have presented a scope to extract the underlying features from the large-scale input images in providing better solutions for tasks such as automatic road detection that come at the cost of time and memory overhead. In this context, we have proposed a three-layer edge-fog-cloud-based intelligent satellite IoT architecture that uses the superpixel-based CNN approach. At the fog layer, the superpixel-based simple linear iterative cluster (SLIC) algorithm uses the images captured by the satellites of the edge level to produce the smaller-sized superpixel images that can be transferred even in a low bandwidth link. The CNN module at the cloud level is then trained with these superpixel images to predict the road networks from these RS images. Two popular road datasets: the DeepGlobe Road dataset and the Massachusetts Road dataset, have been considered to prove the usefulness of the proposed SLIC-CNN architecture in satellite-based IoT platforms to address the problems like RS image-based road extraction. The proposed architecture achieves better performance accuracy than the classical CNN while reducing the incurred overhead by a noticeable limit.
期刊介绍:
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.
The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.