An Initial Study in Wood Tomographic Image Classification using the SVM and CNN Techniques

The internal analysis of wood logs is an essential task in the field of forest assessment. To assist in the identification of anomalies within wood logs, methods from the Non-Destructive Testing area can be used, as the acoustic methods. The ultrasound tomography is an acoustic method that allows to evaluate the internal conditions of wood logs, through the analysis of wave propagation, without being necessary to cause damage to the specimen. The images generated by ultrasound tomography can be improved by spatial interpolation, i.e., estimating the values of wave propagation not measured in the initial examination. In this paper we present an initial study of classification techniques in order to identify tomographic images with anomalies. In our approach we consider three different classifiers: k-Nearest-Neighbor (k-NN), Support Vector Machine (SVM) and Convolutional Neural Network (CNN). Experiments were conducted comparing them by means of metrics obtained from the confusion matrix. We build a dataset with 5000 images using data augmentation process. The quantitative metrics demonstrate the effectiveness of CNN when compared with k-NN and SVM classifiers.