Identification of selected log characteristics from computed tomography images of sugar maple logs using maximum likelihood classifier and textural analysis

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Abstract
In recent years, computed tomography (CT) was investigated to acquire internal log information non-destructively. This paper studied the feasibility of identifying internal log characteristics in CT images by means of maximum likelihood classifier. The log characteristics to be identified include heartwood, sapwood, inner bark, and knots in sugar maple. A total of 100 CT images were sampled from one log to develop the classifier and 20 images were selected from another log for validation. Besides spectral and distance features, textural features were also assessed. In total, nine of them were selected as the input features for the classifier based on the class separability analysis. The classifier developed in this study produced overall accuracies of 79.8% and 72.2% for the training images and the validation images, respectively. This study indicates that the developed maximum likelihood classifier relying on a combination of spectral, textural, and distance features may be applicable to identify the internal log characteristics in the CT images of sugar maple.

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