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OBIA System for Identifying Mesoscale Oceanic Structures in SeaWiFS and MODIS-Aqua Images
dc.contributor.author | Vidal-Fernández, Eva | |
dc.contributor.author | Piedra Fernández, José Antonio | |
dc.contributor.author | Almendros Jiménez, Jesús Manuel | |
dc.contributor.author | Cantón-Garbín, Manuel | |
dc.date.accessioned | 2017-11-08T08:40:01Z | |
dc.date.available | 2017-11-08T08:40:01Z | |
dc.date.issued | 2015 | |
dc.identifier.issn | 1939-1404 | |
dc.identifier.uri | http://hdl.handle.net/10835/5284 | |
dc.description.abstract | The ocean covers over 70% of the surface of our planet and plays a key role in the global climate. Most ocean circulation is mesoscale (scales of 50–500 km and 10–100 days), and the energy in mesoscale circulation is at least one order of magnitude greater than general circulation; therefore, the study of mesoscale oceanic structures (MOS) is crucial to ocean dynamics, making it especially useful for analyzing global changes. The detection of MOS, such as upwellings or eddies, from satellites images is significant for marine environmental studies and coastal resource management. In this paper, we present an object-based image analysis (OBIA) system which segments and classifies regions contained in sea-viewing field-of-view sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS)-Aqua sensor satellite images into MOS. After color clustering and hierarchical data format (HDF) file processing, the OBIA system segments images and extracts image descriptors, producing primary regions. Then, it merges regions, recalculating image descriptors for MOS identification and definition. First, regions are labeled by a human-expert, who identifies MOS: upwellings, eddies, cool, and warm eddies. Labeled regions are then classified by learning algorithms (i.e., decision tree, Bayesian network, artificial neural network, genetic algorithm, and near neighbor algorithm) from selected features. Finally, the OBIA system enables images to be queried from the user interface and retrieved by means of fuzzy descriptors and oceanic structures. We tested our system with images from the Canary Islands and the North West African coast. | es_ES |
dc.language.iso | es | es_ES |
dc.relation | info:eu-repo/grantAgreement/ES/MINECO/TIN2013-41576-R/ES/Evolución de sistemas dinámicos en la nube: Un escenario marco hacia las interfaces de usuario inteligentes/ESDNEMIUI | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3):1256-1265, March 2015 | es_ES |
dc.title | OBIA System for Identifying Mesoscale Oceanic Structures in SeaWiFS and MODIS-Aqua Images | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | http://doi.org/10.1109/JSTARS.2015.2400223 | |
dc.relation.projectID | TIN2013-41576-R | es_ES |