Tree crown delineation on UltraCam-D aerial imagery with SVM classification technique optimised by Taguchi method in Zagros woodlands

Abstract

Support vector machine (SVM) is a superior machine learning methodology with great results in classification of remotely sensed data sets. Determination of optimal parameters applied in SVM classification technique is still vague to some scientists. This study was aimed to detect tree crowns on UltraCam-D (UCD), very high spatial resolution aerial imagery in Zagros woodlands by SVM optimised by Taguchi method. A 500 × 600 m2 plot covered with Persian oak (Quercus brantii var. persica) coppice trees was selected in Zagros woodlands, Iran. The UCD aerial imagery of the plot (0.06-m spatial resolution) was obtained to extract crowns of the trees in this study. The SVM classification technique parameters were optimised by Taguchi method, and thereafter, the imagery was classified with optimal parameters. The results showed that Taguchi method is a robust approach to optimise the combination of parameters of SVM classification technique. It was also concluded that the technique could detect the tree crowns with a KHAT coefficient of 0.961, which showed a great agreement with the observed samples, and overall accuracy of 97.7% that showed the accuracy of the final map. Finally, the authors suggest applying this method to optimise the parameters of classifiers like SVM classification technique.