Wood is usually stored outdoors so that when its hyper-spectral image is picked up, the acquired image is usually disturbed by environmental factors such as illumination, temperature, and humidity. This disturbance may produce the false wood species classification results. To solve this issue, the wood texture feature is extracted in its hyper-spectral image by use of PLS and LBP. This texture feature is then combined with the near infrared spectra of wood hyper-spectral image so that the fused features are sent into SVM and BP neural network classifiers. Experimental results indicate that our scheme can reach to 100% classification accuracy without environmental disturbance. Moreover, to testify our scheme’s robustness in case of illumination variation, a simulation experiment is performed and it indicates that our scheme outperforms the conventional and the state-of-art wood recognition schemes.