Samia Boukir

TL;DR: The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class, and the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination.

TL;DR: New ensemble margin criteria are introduced to evaluate the performance of Random Forests in the context of large area land cover classification and the effect of different training data characteristics (imbalance and mislabelling) on classification accuracy and uncertainty is examined.

TL;DR: This paper presents a new ensemble pruning method which highly reduces the complexity of ensemble methods and performs better than complete bagging in terms of classification accuracy and is a very fast algorithm.

TL;DR: An efficient, quasi-automatic object-based method for change mapping using high-spatial-resolution (HR) (5–10 m) satellite imagery is proposed and highlights the correlation between the ages of trees and their sensitivity to wind.

TL;DR: An automated forest variable estimation scheme based on linear regressions, which outperforms two well-established variable subset selection techniques and relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables.