Wood species identification using spectral reflectance feature and optimal illumination radian design.

We developed a novel wood recognition scheme based on wood surface spectral features that aimed to solve three problems. First was elimination of noise in some bands of wood spectral reflection curves. Second was improvement of wood feature selection based on analysis of wood spectral data. The wood spectral band is 350-2500 nm, a 2150D vector with a spectral sampling interval of 1 nm. We developed a feature selection procedure and a filtering procedure by solving the eigenvalues of the dispersion matrix. Third, we optimized the design for the indoor radian's mounting height. We used a genetic algorithm to solve the optimal radian's height so that the spectral reflection curves had the best classification information for wood species. Experiments on fivecommon wood species in northeast China showed overall recognition accuracy >95 % at optimal recognition velocity. [ABSTRACT FROM AUTHOR]