Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

Abstract

The extraction of burned areas and the monitoring of forest type distribution are often affected by image classification methods. We aim to compare two image classification methods, convolutional neural network (CNN) and support vector machine (SVM), for identification of forest types and burned areas. A single post-fire Landsat 8 OLI image, forest management inventory data, and forest fire data were used to determine the optimal sample dataset. The CNN utilized PSPNet for training, while the ResNet34 served as the skeleton network to identify burned areas and forest types simultaneously. To compare and evaluate the effectiveness of the CNN model, the SVM was also used to classify the Landsat 8 OLI image with the same amount of sample data. The results indicate that the CNN model for per-pixel classification of seven classes (burned area, coniferous forest, broadleaved forest, mixed forest, residential area, water, and the other class) achieved an overall accuracy of 92.25% with a kappa coefficient of 0.8823. In contrast, the overall accuracy of the SVM algorithm was 86.72%, with a kappa coefficient of 0.8219. The results suggest that the CNN can achieve a higher classification accuracy than the SVM, and that the CNN is more reliable to support forest resources monitoring and management after a fire.