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Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are stud...

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Autores principales: Ibarrola-Ulzurrun, Edurne, Gonzalo-Martin, Consuelo, Marcello-Ruiz, Javier, Garcia-Pedrero, Angel, Rodriguez-Esparragon, Dionisio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336000/
https://www.ncbi.nlm.nih.gov/pubmed/28125055
http://dx.doi.org/10.3390/s17020228
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author Ibarrola-Ulzurrun, Edurne
Gonzalo-Martin, Consuelo
Marcello-Ruiz, Javier
Garcia-Pedrero, Angel
Rodriguez-Esparragon, Dionisio
author_facet Ibarrola-Ulzurrun, Edurne
Gonzalo-Martin, Consuelo
Marcello-Ruiz, Javier
Garcia-Pedrero, Angel
Rodriguez-Esparragon, Dionisio
author_sort Ibarrola-Ulzurrun, Edurne
collection PubMed
description Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are studied. As far as these resources’ reduction is concerned, remote sensing and image processing techniques could contribute to the management of these natural resources in a practical and cost-effective way, although some improvements are needed for obtaining a higher quality of the information available. An important quality improvement is the fusion at the pixel level. Hence, the objective of this work is to assess which pansharpening technique provides the best fused image for the different types of ecosystems. After a preliminary evaluation of twelve classic and novel fusion algorithms, a total of four pansharpening algorithms was analyzed using six quality indices. The quality assessment was implemented not only for the whole set of multispectral bands, but also for the subset of spectral bands covered by the wavelength range of the panchromatic image and outside of it. A better quality result is observed in the fused image using only the bands covered by the panchromatic band range. It is important to highlight the use of these techniques not only in land and urban areas, but a novel analysis in areas of shallow water ecosystems. Although the algorithms do not show a high difference in land and coastal areas, coastal ecosystems require simpler algorithms, such as fast intensity hue saturation, whereas more heterogeneous ecosystems need advanced algorithms, as weighted wavelet ‘à trous’ through fractal dimension maps for shrublands and mixed ecosystems. Moreover, quality map analysis was carried out in order to study the fusion result in each band at the local level. Finally, to demonstrate the performance of these pansharpening techniques, advanced Object-Based (OBIA) support vector machine classification was applied, and a thematic map for the shrubland ecosystem was obtained, which corroborates wavelet ‘à trous’ through fractal dimension maps as the best fusion algorithm for this ecosystem.
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spelling pubmed-53360002017-03-16 Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems Ibarrola-Ulzurrun, Edurne Gonzalo-Martin, Consuelo Marcello-Ruiz, Javier Garcia-Pedrero, Angel Rodriguez-Esparragon, Dionisio Sensors (Basel) Article Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are studied. As far as these resources’ reduction is concerned, remote sensing and image processing techniques could contribute to the management of these natural resources in a practical and cost-effective way, although some improvements are needed for obtaining a higher quality of the information available. An important quality improvement is the fusion at the pixel level. Hence, the objective of this work is to assess which pansharpening technique provides the best fused image for the different types of ecosystems. After a preliminary evaluation of twelve classic and novel fusion algorithms, a total of four pansharpening algorithms was analyzed using six quality indices. The quality assessment was implemented not only for the whole set of multispectral bands, but also for the subset of spectral bands covered by the wavelength range of the panchromatic image and outside of it. A better quality result is observed in the fused image using only the bands covered by the panchromatic band range. It is important to highlight the use of these techniques not only in land and urban areas, but a novel analysis in areas of shallow water ecosystems. Although the algorithms do not show a high difference in land and coastal areas, coastal ecosystems require simpler algorithms, such as fast intensity hue saturation, whereas more heterogeneous ecosystems need advanced algorithms, as weighted wavelet ‘à trous’ through fractal dimension maps for shrublands and mixed ecosystems. Moreover, quality map analysis was carried out in order to study the fusion result in each band at the local level. Finally, to demonstrate the performance of these pansharpening techniques, advanced Object-Based (OBIA) support vector machine classification was applied, and a thematic map for the shrubland ecosystem was obtained, which corroborates wavelet ‘à trous’ through fractal dimension maps as the best fusion algorithm for this ecosystem. MDPI 2017-01-25 /pmc/articles/PMC5336000/ /pubmed/28125055 http://dx.doi.org/10.3390/s17020228 Text en © 2017 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ibarrola-Ulzurrun, Edurne
Gonzalo-Martin, Consuelo
Marcello-Ruiz, Javier
Garcia-Pedrero, Angel
Rodriguez-Esparragon, Dionisio
Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems
title Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems
title_full Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems
title_fullStr Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems
title_full_unstemmed Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems
title_short Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems
title_sort fusion of high resolution multispectral imagery in vulnerable coastal and land ecosystems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336000/
https://www.ncbi.nlm.nih.gov/pubmed/28125055
http://dx.doi.org/10.3390/s17020228
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