Algoritmo paralelo para processamento de séries temporais de sensoriamento remoto com aplicação na classificação do uso e cobertura do solo

• Ano de defesa: 2021
• Autor(a) principal: Paiva, Roberto de Urzêda
• Orientador(a): Martins, Wellington Santos
• Banca de defesa: Laureano, Gustavo Teodoro, Martins, Wellington Santos, Ferreira Júnior, Laerte Guimarães, Oliveira, Sávio Salvarino Teles de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: DTW; TWDTW; Rapid-DTW; Meta-características; Programação paralela; GPU; Séries temporais; Sensoriamento remoto; Classificação; Uso e cobertura do solo
• Palavras-chave em Inglês: DTW; TWDTW; Rapid-DTW; Meta-features; Parallel programming; GPU; Time series; Remote sensing; Classification; Land-use and land-cover
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/11297

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.