Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil
نویسندگان
چکیده
Landslide identification is important for understanding their conditioning factors, and constructing susceptibility, risk, vulnerability maps. In remote sensing this can be accomplished manually or through classifiers. This study compares three image classifiers (Maximum Likelihood, Random Forest, Support Vector Machines (SVM)) used in identifying landslides Itaóca (São Paulo, Brazil). Two datasets were used: a RapidEye-5 (5 m) Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) (12.5 m). Seven pixel-based classifications produced, two each classifier binary class that identified only non-landslides. One classification contained five spectral bands (5B), while the other six (6B) included slope derived from DEM. The results validated using Kappa index F1 score. SVM 6B achieved best among validation indices herein. It landslide area of 399,325 m². contribute to mapping tropical environments However, although was successful, with larger areas captured by algorithms, confirming importance conducting further analyses images finer spatial resolution.
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ژورنال
عنوان ژورنال: Brazilian Journal of Geology
سال: 2021
ISSN: ['2317-4889', '2317-4692']
DOI: https://doi.org/10.1590/2317-4889202120200105