Authors:
Lucas Felipe Kunze
;
Thábata Amaral
;
Leonardo Mauro Pereira Moraes
;
Jadson José Monteiro Oliveira
;
Altamir Gomes Bispo Junior
;
Elaine Parros Machado de Sousa
and
Robson Leonardo Ferreira Cordeiro
Affiliation:
University of Sao Paulo, Brazil
Keyword(s):
Data Mining, Classification, NDVI Time Series, Metric Space.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Geographical Information Systems
;
Human-Computer Interaction
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
In Brazil, agribusiness is an important task to the economy, since it provides a substantial part of the country's Gross Domestic Product (GDP). Besides that, interest in biofuels has grown, considering that they viabilize the use of renewable energy. Brazil is the world's largest producer of sugarcane, which enables a large ethanol production. Thus, to monitor agricultural areas is important to support decision making. However, the amount of generated and stored data about these areas has been increasing in such a way that far exceeds the human capacity to manually analyze and extract information from it. That is why automatic and scalable data mining approaches are necessary. This work focuses on the sugarcane classification task, taking as input NDVI time series extracted from remote sensing images. Existing related works propose to analyze non-metric features spaces using the DTW distance function as a basis. Here we demonstrate that analyzing the multidimensional space with Mink
owski distance provides better results, considering a variety of classifiers. XGBoost and kNN, both using L2 distance, performed similarly or better than the DTW-based classifiers in terms of accuracy
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