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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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-979.pdf |
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| author | José Roberto Lomelí-Huerta Juan Pablo Rivera-Caicedo Miguel De-la-Torre Brenda Acevedo-Juárez Jushiro Cepeda-Morales Himer Avila-George |
| author_facet | José Roberto Lomelí-Huerta Juan Pablo Rivera-Caicedo Miguel De-la-Torre Brenda Acevedo-Juárez Jushiro Cepeda-Morales Himer Avila-George |
| author_sort | José Roberto Lomelí-Huerta |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-12T02:50:42Z |
| format | Article |
| id | doaj.art-17d772fecc83403187e7553af47e9a6a |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-12T02:50:42Z |
| publishDate | 2022-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-17d772fecc83403187e7553af47e9a6a2022-12-22T00:40:53ZengPeerJ Inc.PeerJ Computer Science2376-59922022-05-018e97910.7717/peerj-cs.979An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOFJosé Roberto Lomelí-Huerta0Juan Pablo Rivera-Caicedo1Miguel De-la-Torre2Brenda Acevedo-Juárez3Jushiro Cepeda-Morales4Himer Avila-George5Departamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Ameca, Jalisco, MéxicoCONACYT-UAN, Secretaría de Investigación Posgrado, Universidad Autónoma de Nayarit, Tepic, Nayarit, MexicoDepartamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Ameca, Jalisco, MéxicoDepartamento de Ciencias Naturales y Exactas, Universidad de Guadalajara, Ameca, Jalisco, MexicoCentro Nayarita de Innovación y Transferencia de Tecnología A. C., Universidad Autónoma de Nayarit, Tepic, Nayarit, MexicoDepartamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Ameca, Jalisco, MéxicoThis 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.https://peerj.com/articles/cs-979.pdfSatellite imageryMissing dataDINEOFMODISVIIRS |
| spellingShingle | José Roberto Lomelí-Huerta Juan Pablo Rivera-Caicedo Miguel De-la-Torre Brenda Acevedo-Juárez Jushiro Cepeda-Morales Himer Avila-George An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF PeerJ Computer Science Satellite imagery Missing data DINEOF MODIS VIIRS |
| 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 | Satellite imagery Missing data DINEOF MODIS VIIRS |
| url | https://peerj.com/articles/cs-979.pdf |
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