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 classif...

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Bibliographic Details
Published in:International journal of remote sensing 2015-02, Vol.36 (4), p.954-978
Main Authors: Maxwell, A.E., Warner, T.A., Strager, M.P., Conley, J.F., Sharp, A.L.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Classifiers
Complex variables
Image analysis
Image classification
Image detection
Imagery
Lidar
Machine learning
Mathematical analysis
Matrix methods
Mine reclamation
Mining
Reclamation
Regression analysis
Satellite imagery
Spaceborne remote sensing
Statistical methods
Support vector machines
Texture
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Summary: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.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2014.1001086