Open AccessLandslide Scars Detection using Remote Sensing and Pattern Recognition Techniques: Comparison Among Artificial Neural Networks, Gaussian Maximum Likelihood, Random Forest, and Support Vector Machine Classifiers
Author(s) -
Tatiana Dias Tardelli Uehara,
Sabrina Paes Leme Passos Corrêa,
Renata Pacheco Quevedo,
Thales Sehn Körting,
Luciano Vieira Dutra,
Camilo Daleles Rennó
Publication year - 2020
Publication title -
rbc. revista brasileira de cartografia/revista brasileira de cartografia
Language(s) - English
Resource type - Journals
eISSN - 1808-0936
pISSN - 0560-4613
DOI - 10.14393/rbcv72n4-54037
Subject(s) - support vector machine , random forest , landslide , artificial intelligence , computer science , pattern recognition (psychology) , artificial neural network , digital elevation model , remote sensing , feature selection , vegetation (pathology) , geology , geomorphology , medicine , pathology
Landslide inventory is an essential tool to support disaster risk mitigation. The inventory is usually obtained via conventional methods, as visual interpretation of remote sensing images, or semi-automatic methods, through pattern recognition. In this study, four classification algorithms are compared to detect landslides scars: Artificial Neural Network (ANN), Maximum Likelihood (ML), Random Forest (RF) and Support Vector Machine (SVM). From Sentinel-2A imagery and SRTM’s Digital Elevation Model (DEM), vegetation indices and slope features were extracted and selected for two areas at the Rolante River Catchment, in Brazil. The classification products showed that the ML and the RF presented superior results with OA values above 92% for both study areas. These best accuracy’s results were identified in classifications using all attributes as input, so without previous feature selection.