Wood species recognition using hyper-spectral images not sensitive to illumination variation
Received:August 18, 2019  Revised:December 17, 2019  download
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Author NameAffiliationE-mail
WANG Cheng-Kun College of Information and Computer Engineering, Northeast Forestry Univ., Harbin 150040,China 402686820@QQ.COM 
ZHAO Peng College of Information and Computer Engineering, Northeast Forestry Univ., Harbin 150040,China bit_zhao@aliyun.com 
Abstract: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.
keywords:hyper-spectral image  wood species recognition  illumination variation  feature fusion
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Copyright:《Journal of Infrared And Millimeter Waves》