Algoritmo paralelo para processamento de séries temporais de sensoriamento remoto com aplicação na classificação do uso e cobertura do solo
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Data
2021-03-31
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Universidade Federal de Goiás
Resumo
The increase in satellite launches into Earth's orbit in recent years has generated a huge amount of remote
sensing data. These data are used in automated classification approaches, generating land-use and landcover
products for different landscapes around the world. Dynamic Time Warping (DTW) is a well-known
computational method used to measure the similarity between time series, it has been explored in several
algorithms for remote sensing time series analysis. These DTW-based algorithms are capable of generating
similarity measures between the series and pre-established patterns, these measures can be used as metafeatures
to increase the performance of classification models. However, DTW-based algorithms require a lot
of computational resources and have a high execution time, which makes them difficult to use in large
volumes of data. Attempting to avoid this limitation, this article presents a parallel and fully scalable
solution to optimize the construction of meta-features through remote sensing time series. In addition,
results of the application of the meta-features generated in the training and evaluation of classification
models based on Random Forest are presented, allowing the evaluation of the impact of the use of metafeatures
in the automated classification of land-use and land-cover. The results show that both approaches
have led to improvements in execution time and accuracy when compared to traditional strategies and
models.
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Palavras-chave
DTW , TWDTW , Rapid-DTW , Meta-características , Programação paralela , GPU , Séries temporais , Sensoriamento remoto , Classificação , Uso e cobertura do solo , DTW , TWDTW , Rapid-DTW , Meta-features , Parallel programming , GPU , Time series , Remote sensing , Classification , Land-use and land-cover
Citação
PAIVA, R. U. Algoritmo paralelo para processamento de séries temporais de sensoriamento remoto com aplicação na classificação do uso e cobertura do solo. 2021. 66 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2021.