Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to s...
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Online Access: | https://hdl.handle.net/10037/21061 https://doi.org/10.1109/TGRS.2020.3014138 |
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ftunivtroemsoe:oai:munin.uit.no:10037/21061 2023-05-15T14:27:19+02:00 Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection Chlaily, Saloua Mura, Mauro Della Chanussot, Jocelyn Jutten, Christian Gamba, Paolo Marinoni, Andrea 2020-08-17 https://hdl.handle.net/10037/21061 https://doi.org/10.1109/TGRS.2020.3014138 eng eng IEEE IEEE Transactions on Geoscience and Remote Sensing info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ Chlaily S, Mura, Chanussot J, Jutten, Gamba P, Marinoni A. Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection. IEEE Transactions on Geoscience and Remote Sensing. 2020:1-21 FRIDAID 1846079 doi:10.1109/TGRS.2020.3014138 0196-2892 1558-0644 https://hdl.handle.net/10037/21061 openAccess Copyright 2020 IEEE VDP::Technology: 500::Information and communication technology: 550::Geographical information systems: 555 VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Geografiske informasjonssystemer: 555 Journal article Tidsskriftartikkel Peer reviewed acceptedVersion 2020 ftunivtroemsoe https://doi.org/10.1109/TGRS.2020.3014138 2021-06-25T17:58:07Z © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth's surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this article, we aim at predicting the maximum information extraction that can be reached when analyzing a given data set. By means of an asymptotic information theory-based approach, we investigate the reliability and accuracy that can be achieved under optimal conditions for multimodal analysis as a function of data statistics and parameters that characterize the multimodal scenario to be addressed. Our approach leads to the definition of two indices that can be easily computed before the actual processing takes place. Moreover, we report in this article how they can be used for operational use in terms of image selection in order to maximize the robustness of the multimodal analysis, as well as to properly design data collection campaigns for understanding and quantifying physical phenomena. Experimental results show the consistency of our approach. Article in Journal/Newspaper Arctic University of Tromsø: Munin Open Research Archive IEEE Transactions on Geoscience and Remote Sensing 59 7 5598 5618 |
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Open Polar |
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University of Tromsø: Munin Open Research Archive |
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ftunivtroemsoe |
language |
English |
topic |
VDP::Technology: 500::Information and communication technology: 550::Geographical information systems: 555 VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Geografiske informasjonssystemer: 555 |
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VDP::Technology: 500::Information and communication technology: 550::Geographical information systems: 555 VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Geografiske informasjonssystemer: 555 Chlaily, Saloua Mura, Mauro Della Chanussot, Jocelyn Jutten, Christian Gamba, Paolo Marinoni, Andrea Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection |
topic_facet |
VDP::Technology: 500::Information and communication technology: 550::Geographical information systems: 555 VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Geografiske informasjonssystemer: 555 |
description |
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth's surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this article, we aim at predicting the maximum information extraction that can be reached when analyzing a given data set. By means of an asymptotic information theory-based approach, we investigate the reliability and accuracy that can be achieved under optimal conditions for multimodal analysis as a function of data statistics and parameters that characterize the multimodal scenario to be addressed. Our approach leads to the definition of two indices that can be easily computed before the actual processing takes place. Moreover, we report in this article how they can be used for operational use in terms of image selection in order to maximize the robustness of the multimodal analysis, as well as to properly design data collection campaigns for understanding and quantifying physical phenomena. Experimental results show the consistency of our approach. |
format |
Article in Journal/Newspaper |
author |
Chlaily, Saloua Mura, Mauro Della Chanussot, Jocelyn Jutten, Christian Gamba, Paolo Marinoni, Andrea |
author_facet |
Chlaily, Saloua Mura, Mauro Della Chanussot, Jocelyn Jutten, Christian Gamba, Paolo Marinoni, Andrea |
author_sort |
Chlaily, Saloua |
title |
Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection |
title_short |
Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection |
title_full |
Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection |
title_fullStr |
Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection |
title_full_unstemmed |
Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection |
title_sort |
capacity and limits of multimodal remote sensing: theoretical aspects and automatic information theory-based image selection |
publisher |
IEEE |
publishDate |
2020 |
url |
https://hdl.handle.net/10037/21061 https://doi.org/10.1109/TGRS.2020.3014138 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
IEEE Transactions on Geoscience and Remote Sensing info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ Chlaily S, Mura, Chanussot J, Jutten, Gamba P, Marinoni A. Capacity and Limits of Multimodal Remote Sensing: Theoretical Aspects and Automatic Information Theory-Based Image Selection. IEEE Transactions on Geoscience and Remote Sensing. 2020:1-21 FRIDAID 1846079 doi:10.1109/TGRS.2020.3014138 0196-2892 1558-0644 https://hdl.handle.net/10037/21061 |
op_rights |
openAccess Copyright 2020 IEEE |
op_doi |
https://doi.org/10.1109/TGRS.2020.3014138 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
container_volume |
59 |
container_issue |
7 |
container_start_page |
5598 |
op_container_end_page |
5618 |
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1766300995601563648 |