Similarity metrics enforcement in seasonal agriculture areas classification

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dc.contributor.authorSantos M.A.S.
dc.contributor.authorAssad E.D.
dc.contributor.authorGurgel A.C.
dc.contributor.authorOmar N.
dc.date.accessioned2024-03-12T23:47:48Z
dc.date.available2024-03-12T23:47:48Z
dc.date.issued2020
dc.description.abstract© 2020 by the authors.Accurate identification of agriculture areas is a key piece in the building blocks strategy of environment and economics resources management. The challenge requires one to deal with landscape complexity, sensors and data acquisition limitations through a proper computational approach to timely deliver accurate information. In this paper, a Machine Learning (ML) based method to enhance the classification process of areas dedicated to seasonal crops (row crops) is proposed. To this objective, a broad exploration of data from Moderate Resolution Imaging Spectro-radiometer sensors (MODIS) was made using pixel time-series combined with time-series similarity metrics. The experiment was performed in Brazil, covered 61% of the total agriculture areas, five different states specifically selected to demonstrate biome differences and the country's diversity. The validation was made against independent data from EMBRAPA (Brazilian Agriculture Research Corporation), RapidEye Sensor Scene Maps. For the eight tested algorithms, the results were enhanced and demonstrate that the method can rate the classification accuracy up to 98.5%, average value for the tested algorithms. The process can be used to timely monitor large areas dedicated to row crops and enables the application of state of art classification techniques, two levels classification process, to identify crops according to each specific need within the areas.
dc.description.issuenumber11
dc.description.volume12
dc.identifier.doi10.3390/rs12111791
dc.identifier.issn2072-4292
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34970
dc.relation.ispartofRemote Sensing
dc.rightsAcesso Aberto
dc.subject.otherlanguageAgriculture
dc.subject.otherlanguageLand use dynamics
dc.subject.otherlanguageMachine learning
dc.subject.otherlanguageRemote sensing
dc.subject.otherlanguageTime series similarity metrics
dc.titleSimilarity metrics enforcement in seasonal agriculture areas classification
dc.typeArtigo
local.scopus.citations1
local.scopus.eid2-s2.0-85086425680
local.scopus.subjectClassification accuracy
local.scopus.subjectClassification process
local.scopus.subjectClassification technique
local.scopus.subjectComputational approach
local.scopus.subjectLandscape complexity
local.scopus.subjectModerate resolution
local.scopus.subjectResources management
local.scopus.subjectSpectro-radiometers
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086425680&origin=inward
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