Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images
By Edwin Raczko, Bogdan Zagajewski. 2017
338 citations recorded by the source corpus.
ABSTRACT Knowledge of tree species composition in a forest is an important topic in forest management. Accurate tree species maps allow for much more detailed and in-depth analysis of biophysical forest variables. The paper presents a comparison of three classification algorithms: support vector machines (SVM), random forest (RF) and artificial neural networks (ANN) for tree species classification using airborne hyperspectral data from the Airborne Prism EXperiment sensor. The aim of this paper is to evaluate the three nonparametric classification algorithms (SVM, RF and ANN) in an attempt to classify the five most common tree species of the Szklarska Poręba area: spruce (Picea alba L. Karst), larch (Larix decidua Mill.), alder (Alnus Mill), beech (Fagus sylvatica L.) and birch (Betula pendula Roth). To avoid human introduced biases a 0.632 bootstrap procedure was used during evaluation of each compared classifier. Of all compared classification results, ANN achieved the highest median overall classification accuracy (77%) followed by SVM with 68% and RF with 62%. Analysis of the stability of results concluded that RF and SVM had the lowest variance of overall accuracy and kappa coefficient (12 percentage points) while ANN had 15 percentage points variance in results.
Subfields: Ecology, Environmental Engineering, Plant Science
View Paper: https://www.semanticscholar.org/paper/76bb541b3350e0c9d282288695a21560d370744a
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Citations in the corpus
- Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery (2020, methodology): “Two parameters are required for the RF model, namely, the number of predictors that are considered at each fork of the tree (mtry) and the number of random trees assembled during model building (ntree)([33]).”
- Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, background): “Since the algorithm falls into the “embarrassingly parallel” category, one can run several random forests on different machines and then aggregate the votes components to get the final result [52].”
- Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations - A Review (2020, background): “13 SVM, RF, ANN ANN North Western part of Karkonosze National Park, Poland [105]”