Wood species identification using feature-level fusion scheme

In this paper, we propose a robust wood species identification scheme by using a feature-level fusion scheme. First, a novel wood feature acquirement system is devised, which can get the curve of 1D wood spectral reflectance ratio and the 2D wood surface color image. Second, the 4 wood color features, the 4 principal texture features, the 4 secondary texture features and the 4 spectral features are established, respectively. Third, a fuzzy BP neural network is proposed to perform the classification work, which consists of 4 sub-networks based on the color feature, texture feature and spectral feature. We have experimentally proved that this scheme improves the mean recognition accuracy to approximately 90% for 5 wood species. Moreover, our feature-level fusion scheme is superior to the recognition schemes which use color feature and texture feature.