Automatic landslide recognition through Optimum-Path Forest

• Autor(a) principal: Pisani, R. [UNESP]
• Data de Publicação: 2012
• Outros Autores: Riedel, P. [UNESP], Costa, K. [UNESP], Nakamura, R. [UNESP], Pereira, C. [UNESP], Rosa, G. [UNESP], Papa, J. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/IGARSS.2012.6352681; http://hdl.handle.net/11449/73818
• topic: Automatic recognition; Bayesian classifier; Cross validation; Kernel mapping; Optimum-path forests; Radial basis functions; Recognition rates; Supervised classification; Geology; Radial basis function networks; Remote sensing; Landslides

In this paper we shed light over the problem of landslide automatic recognition using supervised classification, and we also introduced the OPF classifier in this context. We employed two images acquired from Geoeye-MS satellite at March-2010 in the northwest (high steep areas) and north sides (pipeline area) covering the area of Duque de Caxias city, Rio de Janeiro State, Brazil. The landslide recognition rate has been assessed through a cross-validation with 10 runnings. In regard to the classifiers, we have used OPF against SVM with Radial Basis Function for kernel mapping and a Bayesian classifier. We can conclude that OPF, Bayes and SVM achieved high recognition rates, being OPF the fastest approach. © 2012 IEEE.