A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery

dc.creatorRomani, LAS
dc.creatorde Avila, AMH
dc.creatorChino, DYT
dc.creatorZullo, J
dc.creatorChbeir, R
dc.creatorTraina, C
dc.creatorTraina, AJM
dc.date2013
dc.dateJAN
dc.date2014-08-01T18:16:37Z
dc.date2015-11-26T17:55:54Z
dc.date2014-08-01T18:16:37Z
dc.date2015-11-26T17:55:54Z
dc.date.accessioned2018-03-29T00:39:35Z
dc.date.available2018-03-29T00:39:35Z
dc.identifierIeee Transactions On Geoscience And Remote Sensing. Ieee-inst Electrical Electronics Engineers Inc, v. 51, n. 1, n. 140, n. 150, 2013.
dc.identifier0196-2892
dc.identifierWOS:000313963700016
dc.identifier10.1109/TGRS.2012.2199501
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/76432
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/76432
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1291129
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionIn this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts.
dc.description51
dc.description1
dc.description1
dc.description140
dc.description150
dc.descriptionEmbrapa
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionSticAmsud
dc.descriptionMicrosoft Research
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.languageen
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.publisherPiscataway
dc.publisherEUA
dc.relationIeee Transactions On Geoscience And Remote Sensing
dc.relationIEEE Trans. Geosci. Remote Sensing
dc.rightsfechado
dc.rightshttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dc.sourceWeb of Science
dc.subjectAssociation rules
dc.subjectimage information mining
dc.subjectNOAA-AVHRR images
dc.subjectsequential patterns
dc.subjectAvhrr Data
dc.subjectSequences
dc.subjectSystem
dc.subjectNavigation
dc.subjectPatterns
dc.subjectArchives
dc.titleA New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución