Data/hora: 16/06/2024 22:48:33

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 14/09/2021
Data da última atualização: | 14/09/2021
Autoria: | SILVA, J. P. da; ZULLO JÚNIOR, J.; ROMANI, L. A. S.
Afiliação: | JOÃO PAULO DA SILVA, Feagri/Unicamp; JURANDIR ZULLO JÚNIOR, UNICAMP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA.
Título: | A time series mining approach for agricultural area detection.
Ano de publicação: | 2020
Fonte/Imprenta: | IEEE Transactions on Big Data, v. 6, n. 3, p. 537-546, Sept. 2020.
DOI: | 10.1109/TBDATA.2019.2913402
Idioma: | Inglês
Conteúdo: | Abstract-Acquiring meaningful data to be employed in building training sets for classification models is a costly task, both in terms of difficult to find suitable samples as well as their quantity. In this sense, Active Learning (AL) improves the training set building by providing an efficient way to select only essential data to be attached to the training set, consequently reducing its size and even enhancing model's accuracy, when compared to random sample selection. In this paper, we proposed a framework for time series classification in order to monitor sugarcane area in Sao Paulo, Brazil. The AL approach consisted of selecting seasonal time series information from less than 1 percent of each class' pixels to build the training set and evaluate this selection by an expert user supported by distance measurements, repeating this process until both distance measurement thresholds were satisfied. In most years, the classification results presented about 90 percent of correlation with official estimates based on both traditional and satellite image analysis methods. This framework can then help Land Use Change (LUC) monitoring as it produced similar results compared to other methods that demands more human and financial resources to be adopted.
Palavras-Chave: | Active Learning; Análise de séries temporais; Classificação de pixel; Pixel classification.
Thesagro: | Agricultura; Meio Ambiente; Sensoriamento Remoto.
Thesaurus Nal: | Agriculture; Environment; Remote sensing; Time series analysis.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (20948-1) - UPC - AP - DD -  -  -  - DIS