木材往往堆积在室外，在对木材样本采集高光谱图像时往往会受到外界因素（光照、温度、湿度）的影响，从而造成木材树种的误判。为了解决这一问题，本文利用PLS（Pattern Lacunarity Spectrum）和LBP（Local Binary Pattern）对木材横截面的高光谱图像的纹理信息进行了特征提取，而后将高光谱图像的近红外光谱与纹理特征相融合，并以融合后的新特征作为识别的依据，最后使用SVM（Support Vector Machine）和BP（Back Propagation）神经网络两种分类器对木材树种进行了识别，实验表明该算法在无干扰情况下可拥有最高100%的识别正确率效果。为了验证该算法可以在高光谱图像失真的情况下依然可以对木材进行正确的识别，本文仿真了光照变化对高光谱图像的影响，并对比了影响前后的识别正确率，结果显示该算法可以在高光谱图像失真的情况下对木材的树种进行正确的识别，优于传统的和近期主流的木材树种分类算法。

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.