Similar Wood Species Classification Within Pterocarpus Genus Using Feature Fusion
WANG Cheng-kun2, 3, ZHAO Peng1, 2*, LI Xiang-hua2
1. College of Computer Science and Electronics, Guangxi University of Science and Technology, Liuzhou 545006, China
2. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
3. College of Electronic and Telecommunication Engineering, Heilongjiang University of Science and Technology, Harbin 150022, China
Abstract：There are much rare wood in the Pterocarpus genus. Rosewood is very similar to different tree species. Traditional wood identification methods are mainly based on wood anatomy, and the wood species are judged by observing the structural characteristics of wood slices. Although this method has a high identification accuracy, its identification process is relatively complex, and the technical difficulty is relatively high. Corresponding to wood anatomy is the identification method of wood tree species using image or spectral information. Although this kind of method has a relatively simple identification technology, it often fails to achieve a good identification effect in identifying similar wood species belonging to the same genus. This paper proposes a wood species identification method based on the fusion of spectral features and texture features of wood section. This method has a simple identification process, a high degree of automation, and a high identification accuracy. First collected by digital camera and a spectrometer wood, slice image information and spectral information, and then respectively using texture feature extraction method and spectrum feature extraction method to extract the characteristics of two kinds of the feature vector, then using the feature level fusion method based on canonical correlation analysis to these two characteristics vector fusion, finally using support vector machine (SVM) for the fusion of feature vector classification recognition. In order to verify the effectiveness of the method, three sections of 5 species of Rosewood species commonly found in the market were taken as research objects to identify these wood species. The experimental results show that the highest recognition accuracy is 80.00% when texture features are used alone, 94.40% when spectral features are used alone, and 99.20% when fused features are used. In this paper, these 5 wood species were mixed with 30 other wood species, and the number of mixed wood samples could reach 1 750. The experimental results show that the method can identify 35 wood species, including Rosewood, and the accuracy rate is 98.29%. To sum up, the texture features and spectral features of wood can effectively complement each other to further improve the recognition accuracy. At the end of this paper, the proposed method is compared with the current mainstream method, and the results show that the wood species identification method described in this paper is higher than the current mainstream method.
Key words：Same genus wood; Tree species identification; Spectral features; Textural features; Feature fusion
王承琨，赵 鹏，李祥华. 采用特征融合的紫檀属内相似树种识别方法研究[J]. 光谱学与光谱分析, 2022, 42(07): 2247-2254.
WANG Cheng-kun, ZHAO Peng, LI Xiang-hua. Similar Wood Species Classification Within Pterocarpus Genus Using Feature Fusion. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2247-2254.
[1] de Geus A R, da Silva S F, Gontijo A B, et al. Multimedia Tools and Applications, 2020, 79(45): 34513.
[2] Zamri M I P, Cordova F, Khairuddin A S M, et al. Computers and Electronics in Agriculture, 2016, 124: 227.
[3] Rosli N R, Khairuddin U, Yusof R, et al. ELEKTRIKA-Journal of Electrical Engineering, 2019, 18(3-2): 1.
[4] Oktaria A S, Prakasa E, Suhartono E. Journal of Information Technology and Computer Science, 2019, 4(3): 274.
[5] Ibrahim I, Khairuddin A S M, Arof H, et al. European Journal of Wood and Wood Products, 2018, 76(1): 345.
[6] Yusof R, Khalid M, Khairuddin A S M. Computers and Electronics in Agriculture, 2013, 93: 68.
[7] Doshi N P, Schaefer G. A Comparative Analysis of Local Binary Pattern Texture Classification. IEEE Conference on Visual Communications and Image Processing. IEEE, 2012.
[8] Ibrahim I, Khairuddin A S M, Talip M S A, et al. Wood Science and Technology, 2017, 51(2): 431.
[9] Xu Y, Ji H, Fermüller C. International Journal of Computer Vision, 2009, 83(1): 85.
[10] Soares S F C, Gomes A A, Araujo M C U, et al. Trends in Analytical Chemistry, 2013, 42: 84.
[11] Sun Q S, Zeng S G, Liu Y, et al. Pattern Recognition, 2005, 38(12): 2437.
[12] Haghighat M, Abdel-Mottaleb M, Alhalabi W. Expert Systems with Applications, 2016, 47: 23.
[13] Browne M W. Journal of Mathematical Psychology, 2000, 44(1): 108.
