RIVER SEDIMENT YIELD CLASSIFICATION USING REMOTE SENSING IMAGERY

Author(s)

Pisani, R. ; Costa, K. ; Rosa, G. ; Pereira, D. ; Papa, J. ; Tavares, J. M. R. S. ; IEEE

• Document type: Journal article
• Source: 2016 9TH IAPR WORKSHOP ON PATTERN RECOGNITION IN REMOTE SENSING (PRRS); v. N/A, p. 6-pg., 2016-01-01.

Abstract

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. (AU)

• FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/00801-9 - About anomaly detection in computer networks using optimum-path forest: advances and applications in computer networks
• Grantee: Kelton Augusto Pontara da Costa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/25739-4 - On the Study of Semantics in Deep Learning Models
• Grantee: Gustavo Henrique de Rosa
• Support Opportunities: Scholarships in Brazil - Master