Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy
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Campo DC | Valor | Idioma |
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dc.contributor | Señales, Sistemas y Telecomunicación | es |
dc.contributor.author | Vicente-Guijalba, Fernando | - |
dc.contributor.author | Martínez Marín, Tomás | - |
dc.contributor.author | Lopez-Sanchez, Juan M. | - |
dc.contributor.other | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | es |
dc.contributor.other | Universidad de Alicante. Instituto Universitario de Investigación Informática | es |
dc.date.accessioned | 2014-05-30T10:58:47Z | - |
dc.date.available | 2014-05-30T10:58:47Z | - |
dc.date.issued | 2014-06 | - |
dc.identifier.citation | IEEE Geoscience and Remote Sensing Letters. 2014, 11(6): 1081-1085. doi:10.1109/LGRS.2013.2286214 | es |
dc.identifier.issn | 1545-598X (Print) | - |
dc.identifier.issn | 1558-0571 (Online) | - |
dc.identifier.uri | http://hdl.handle.net/10045/37746 | - |
dc.description.abstract | In this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is shown. | es |
dc.description.sponsorship | This project was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and in part by EU FEDER under Project TEC2011-28201-C02-02. | es |
dc.language | eng | es |
dc.publisher | IEEE | es |
dc.rights | © Copyright 2014 IEEE | es |
dc.subject | Agriculture | es |
dc.subject | Kalman filter | es |
dc.subject | Multitemporal | es |
dc.subject | Phenology | es |
dc.subject | Polarimetry | es |
dc.subject | Rice | es |
dc.subject | Synthetic aperture radar (SAR) | es |
dc.subject.other | Teoría de la Señal y Comunicaciones | es |
dc.title | Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy | es |
dc.type | info:eu-repo/semantics/article | es |
dc.peerreviewed | si | es |
dc.identifier.doi | 10.1109/LGRS.2013.2286214 | - |
dc.relation.publisherversion | http://dx.doi.org/10.1109/LGRS.2013.2286214 | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//TEC2011-28201-C02-02 | - |
Aparece en las colecciones: | INV - SST - Artículos de Revistas |
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2014_Vicente-Guijalba_etal_IEEE-GRSL_final.pdf | Versión final (acceso restringido) | 583,27 kB | Adobe PDF | Abrir Solicitar una copia |
2014_Vicente-Guijalba_etal_IEEE-GRSL.pdf | Versión revisada (acceso abierto) | 807,49 kB | Adobe PDF | Abrir Vista previa |
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