A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization
When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on mod...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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Online Access: | https://hdl.handle.net/10037/23528 https://doi.org/10.1109/JSTARS.2021.3124308 |
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ftunivtroemsoe:oai:munin.uit.no:10037/23528 2023-06-18T03:38:29+02:00 A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization Khachatrian, Eduard Chlaily, Saloua Eltoft, Torbjørn Marinoni, Andrea 2021-11-13 https://hdl.handle.net/10037/23528 https://doi.org/10.1109/JSTARS.2021.3124308 eng eng IEEE Khachatrian, E. (2023). Multimodal Integrated Remote Sensing for Arctic Sea Ice Monitoring. (Doctoral thesis). https://hdl.handle.net/10037/29338 . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ Khachatrian, Chlaily, Eltoft, Marinoni. A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021;14:11546-11566 FRIDAID 1955100 doi:10.1109/JSTARS.2021.3124308 1939-1404 2151-1535 https://hdl.handle.net/10037/23528 openAccess Copyright 2021 The Author(s) VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2021 ftunivtroemsoe https://doi.org/10.1109/JSTARS.2021.3124308 2023-06-07T23:06:22Z When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on modeling the physical phenomena occurring on the Earth’s surface. In this article, we introduce a flexible and efficient method based on graph Laplacians for information selection at different levels of data fusion. The proposed approach combines data structure and information content to address the limitations of existing graph-Laplacian-based methods in dealing with heterogeneous datasets. Moreover, it adapts the selection to each homogenous area of the considered images according to their underlying properties. Experimental tests carried out on several multivariate remote sensing datasets show the consistency of the proposed approach. Article in Journal/Newspaper Arctic University of Tromsø: Munin Open Research Archive IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 11546 11566 |
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University of Tromsø: Munin Open Research Archive |
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ftunivtroemsoe |
language |
English |
topic |
VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 |
spellingShingle |
VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 Khachatrian, Eduard Chlaily, Saloua Eltoft, Torbjørn Marinoni, Andrea A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization |
topic_facet |
VDP::Mathematics and natural science: 400::Physics: 430 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 |
description |
When dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on modeling the physical phenomena occurring on the Earth’s surface. In this article, we introduce a flexible and efficient method based on graph Laplacians for information selection at different levels of data fusion. The proposed approach combines data structure and information content to address the limitations of existing graph-Laplacian-based methods in dealing with heterogeneous datasets. Moreover, it adapts the selection to each homogenous area of the considered images according to their underlying properties. Experimental tests carried out on several multivariate remote sensing datasets show the consistency of the proposed approach. |
format |
Article in Journal/Newspaper |
author |
Khachatrian, Eduard Chlaily, Saloua Eltoft, Torbjørn Marinoni, Andrea |
author_facet |
Khachatrian, Eduard Chlaily, Saloua Eltoft, Torbjørn Marinoni, Andrea |
author_sort |
Khachatrian, Eduard |
title |
A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization |
title_short |
A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization |
title_full |
A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization |
title_fullStr |
A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization |
title_full_unstemmed |
A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization |
title_sort |
multimodal feature selection method for remote sensing data analysis based on double graph laplacian diagonalization |
publisher |
IEEE |
publishDate |
2021 |
url |
https://hdl.handle.net/10037/23528 https://doi.org/10.1109/JSTARS.2021.3124308 |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Khachatrian, E. (2023). Multimodal Integrated Remote Sensing for Arctic Sea Ice Monitoring. (Doctoral thesis). https://hdl.handle.net/10037/29338 . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ Khachatrian, Chlaily, Eltoft, Marinoni. A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021;14:11546-11566 FRIDAID 1955100 doi:10.1109/JSTARS.2021.3124308 1939-1404 2151-1535 https://hdl.handle.net/10037/23528 |
op_rights |
openAccess Copyright 2021 The Author(s) |
op_doi |
https://doi.org/10.1109/JSTARS.2021.3124308 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
14 |
container_start_page |
11546 |
op_container_end_page |
11566 |
_version_ |
1769003479658921984 |