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An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF

This paper proposes an approach to fill in missing data from satellite images using data-intensive computing platforms. The proposed approach merges satellite imagery from diverse sources to reduce the impact of the holes in images that result from acquisition conditions: occlusion, the satellite tr...

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Autores principales: Lomelí-Huerta, José Roberto, Rivera-Caicedo, Juan Pablo, De-la-Torre, Miguel, Acevedo-Juárez, Brenda, Cepeda-Morales, Jushiro, Avila-George, Himer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138164/
https://www.ncbi.nlm.nih.gov/pubmed/35634099
http://dx.doi.org/10.7717/peerj-cs.979
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author Lomelí-Huerta, José Roberto
Rivera-Caicedo, Juan Pablo
De-la-Torre, Miguel
Acevedo-Juárez, Brenda
Cepeda-Morales, Jushiro
Avila-George, Himer
author_facet Lomelí-Huerta, José Roberto
Rivera-Caicedo, Juan Pablo
De-la-Torre, Miguel
Acevedo-Juárez, Brenda
Cepeda-Morales, Jushiro
Avila-George, Himer
author_sort Lomelí-Huerta, José Roberto
collection PubMed
description This paper proposes an approach to fill in missing data from satellite images using data-intensive computing platforms. The proposed approach merges satellite imagery from diverse sources to reduce the impact of the holes in images that result from acquisition conditions: occlusion, the satellite trajectory, sunlight, among others. The amount of computation effort derived from the use of large high-resolution images is addressed by data-intensive computing techniques that assume an underlying cluster architecture. As a start, satellite data from the region of study are automatically downloaded; then, data from different sensors are corrected and merged to obtain an orthomosaic; finally, the orthomosaic is split into user-defined segments to fill in missing data, and filled segments are assembled to produce an orthomosaic with a reduced amount of missing data. As a proof of concept, the proposed data-intensive approach was implemented to study the concentration of chlorophyll at the Mexican oceans by merging data from MODIS-TERRA, MODIS-AQUA, VIIRS-SNPP, and VIIRS-JPSS-1 sensors. The results revealed that the proposed approach produces results that are similar to state-of-the-art approaches to estimate chlorophyll concentration but avoid memory overflow with large images. Visual and statistical comparison of the resulting images revealed that the proposed approach provides a more accurate estimation of chlorophyll concentration when compared to the mean of pixels method alone.
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spelling pubmed-91381642022-05-28 An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF Lomelí-Huerta, José Roberto Rivera-Caicedo, Juan Pablo De-la-Torre, Miguel Acevedo-Juárez, Brenda Cepeda-Morales, Jushiro Avila-George, Himer PeerJ Comput Sci Spatial and Geographic Information Systems This paper proposes an approach to fill in missing data from satellite images using data-intensive computing platforms. The proposed approach merges satellite imagery from diverse sources to reduce the impact of the holes in images that result from acquisition conditions: occlusion, the satellite trajectory, sunlight, among others. The amount of computation effort derived from the use of large high-resolution images is addressed by data-intensive computing techniques that assume an underlying cluster architecture. As a start, satellite data from the region of study are automatically downloaded; then, data from different sensors are corrected and merged to obtain an orthomosaic; finally, the orthomosaic is split into user-defined segments to fill in missing data, and filled segments are assembled to produce an orthomosaic with a reduced amount of missing data. As a proof of concept, the proposed data-intensive approach was implemented to study the concentration of chlorophyll at the Mexican oceans by merging data from MODIS-TERRA, MODIS-AQUA, VIIRS-SNPP, and VIIRS-JPSS-1 sensors. The results revealed that the proposed approach produces results that are similar to state-of-the-art approaches to estimate chlorophyll concentration but avoid memory overflow with large images. Visual and statistical comparison of the resulting images revealed that the proposed approach provides a more accurate estimation of chlorophyll concentration when compared to the mean of pixels method alone. PeerJ Inc. 2022-05-13 /pmc/articles/PMC9138164/ /pubmed/35634099 http://dx.doi.org/10.7717/peerj-cs.979 Text en © 2022 Lomelí-Huerta et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Spatial and Geographic Information Systems
Lomelí-Huerta, José Roberto
Rivera-Caicedo, Juan Pablo
De-la-Torre, Miguel
Acevedo-Juárez, Brenda
Cepeda-Morales, Jushiro
Avila-George, Himer
An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF
title An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF
title_full An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF
title_fullStr An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF
title_full_unstemmed An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF
title_short An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF
title_sort approach to fill in missing data from satellite imagery using data-intensive computing and dineof
topic Spatial and Geographic Information Systems
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138164/
https://www.ncbi.nlm.nih.gov/pubmed/35634099
http://dx.doi.org/10.7717/peerj-cs.979
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