Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation

This study investigates machine-learning algorithms and measures derived from RapidEye satellite imagery and light detection and ranging (lidar) data for geographic object-based image analysis classification of mining and mine reclamation. Support vector machines, random forests, and boosted classification and regression trees classification algorithms were assessed and compared with the k-nearest neighbour (k-NN) classifier. For geographic object-based image analysis classification of mine landscapes, the use of disparate data (i.e. lidar data) improved overall accuracy, whereas the use of complex, object-oriented variables such as object geometry measures, first-order texture, and second-order texture from the grey-level co-occurrence matrix decreased or did not improve the classification accuracy. Support vector machines generally outperformed k-NN and the ensemble tree classifiers when only using the band means. With the incorporation of lidar-descriptive statistics, all four algorithms provided statistically comparable accuracies. K-NN suffered reduced classification accuracy with high-dimensional feature spaces, suggesting that a more complex machine-learning algorithm may be more appropriate when a large number of predictor variables are used.