River sediment yield classification using remote sensing imagery

• Autor(a) principal: Pisani, R.
• Data de Publicação: 2017
• Outros Autores: Costa, K. [UNESP], Rosa, G. [UNESP], Pereira, D. [UNESP], Papa, J. [UNESP], Tavares, J. M.R.S.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/PRRS.2016.7867014; http://hdl.handle.net/11449/220822
• topic: Machine Learning; Optimum-Path Forest; Sediment Yield

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 stateof-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.