An assessment of the effectiveness of a random forest classifier for land-cover classification

By Victor Francisco Rodriguez-Galiano, B. Ghimire, J. Rogan, Mario Chica-Olmo, J. P. Rigol-Sánchez. 2012

2,705 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Media Technology

View Paper: https://www.semanticscholar.org/paper/13dd43463710f0b5039fb93f7a9eda29d2d72f89

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In Ecology

In Global and Planetary Change

In Media Technology

Citations in the corpus

Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods (2015, result): “The study similar to other studies has shown that the use of split lower than 60–40% or greater than 70–30% for defoliation classification is not recommended (Stumpf and Kerle 2011; Rodriguez-Galiano et al. 2012).”
A survey of multiple classifier systems as hybrid systems (2014, methodology): “It has been applied to estimate land cover on Landsat data over Granada, Spain [168] and multisource data in a Colorado mountainous area [169].”
UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis (2015, methodology): “Random Forest only needs to specify two parameters, ntree, the number of trees to grow into a whole forest, and mty, the number of randomly selected predictor variables [37].”
Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery (2015, background): “[38] showed that random forests are easier to set up and do not suffer from over-learning.”
Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China (2015, methodology): “Only two parameters are utilized to construct a Random Forest classifier: ntree, the number of trees to grow into a whole forest, and mty, the number of randomly selected predictor variables [20].”
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016, methodology): “The most common strategy consists in selecting images acquired on two different seasons (Rogan et al., 2002; Rodriguez-Galiano et al., 2012).”
Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data (2012, background): “A detailed description of RF can be found in [16,17] and in particular for remote sensing applications in [18,22].”
Developments in Landsat Land Cover Classification Methods: A Review (2017, background): “Non-parametric classifiers such as SVM and ANN have proved to be more useful because they do not base classification on a normality assumption or statistical parameters [56].”
Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest (2020, methodology): “RF is a nonparametric analysis that uses DTs, with their class values assigned according to the maximum number of forecast votes through the DTs [30], and with more predictive power in comparison to classic regression methods [31,32], in essence to efficiently handling many variables that do not adj”