RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY
Nenhuma Miniatura disponível
Data
2016-01-01
Autores
Pisani, R.
Costa, K. [UNESP]
Rosa, G. [UNESP]
Pereira, D. [UNESP]
Papa, J. [UNESP]
Tavares, J. M. R. S.
IEEE
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee
Resumo
The monitoring of water quality is essencial to the mankind, since we strongly depend on such resource for living and working. The presence of sediments in rivers usually indicates changes in the land use, which can affect the quality of water and the lifetime of hydroelectric power plants. In countries like Brazil, where more than 70% of the energy comes from the water, it is crucial to keep monitoring the sediment yield in rivers and lakes. In this work, we evaluate some state-of- the-art supervised pattern recognition techniques to classify different levels of sediments in Brazilian rivers using satellite images, as well as we make available an annotated dataset composed of two images to foster the related research.
Descrição
Palavras-chave
Sediment Yield, Machine Learning, Optimum-Path Forest
Como citar
2016 9th Iapr Workshop On Pattern Recognition In Remote Sensing (prrs). New York: Ieee, 6 p., 2016.