Hyperspectral Bambusoideae Discrimination Based on Mann-Whitney Non-Parametric Test and SVM
CHEN Yong-gang, DING Li-xia, GE Hong-li, ZHANG Mao-zhen, HU Yun
Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, College of Environmental Science and Technology, Zhejiang Agriculture and Forestry University, Lin’an 311300, China
Abstract：In the present study, based on the leaf-level hyperspectral data of MaoZhu, LeiZhu and XiaoShunZhu, We come up with two solutions to discrimination through the theory of non-parametric test and pattern recognition; the first one is that optimal discriminating band between bambusoideae species is extracted by Mann-Whitney non-parametric test, the other is that bambusoideae species is discriminated by the support vector machine. The research results showed that (1) the optimal discriminating band between MaoZhu and LeiZhu is around 503~655, 689~732, 757~1 000, 1 038~1 084, 1 238~1 311, 1 404~1 591, 1 682~1 800, 1 856~1 904, and 1 923~2 500 nm; the optimal discriminating band between MaoZhu and XiaoShunZhu is around 350~386, 731~1 430, 1 584~1 687, and 1 796~1 873 nm; the optimal discriminating band between LeiZhu and XiaoShunZhu is around 355~356, 498~662, 689~745, and 1 344~2 500 nm; and it can eliminate 30.0%, 57.7%, and 35.8% of the invalid distinction between bands by Mann-Whitney non-parametric test method. (2) In these optimal discriminating bands, we found that the accuracy of bambusoideae discrimination is 98.4%, 93.5%, and 95.1%, the generalization accuracy is 93.3%, 90.0%, and 86.7% by sequential minimal optimization algorithm. It indicates that this method is valid for selecting feature band and discriminating bambusoideae species.
[1] De Backer S, Kempeneers P, Debruyn W, et al. Geoscience and Remote Sensing Letters, IEEE, 2005, 2(3): 319. [2] Guo B, Gunn S R, Damper R I, et al. Geoscience and Remote Sensing Letters, IEEE, 2006, 3(4): 522. [3] Archibald R, Fann G. IEEE Geoscience and Remote Sensing Letters, 2007, 4(4): 674. [4] Rosner B A. Fundamentals of Biostatistics: Duxbury Resource Center, 2006. [5] Wasserman L. All of Nonparametric Statistics: Springer-Verlag New York Inc, 2006. [6] Mann H B, Whitney D R. The Annals of Mathematical Statistics, 1947, 18(1): 50. [7] Soman K P, Diwakar S, Ajay V. Insight into Data Mining: Theory and Practice: PHI Learning Pvt. Ltd., 2006. [8] Tan P N, Steinbach M, Kumar V. Introduction to Data Mining: Pearson Addison Wesley Boston, 2006. [9] Han J, Kamber M. Data Mining: Concepts and Techniques 2nd ed: Morgan Kaufmann, 2006. [10] Cortes C, Vapnik V. Machine Learning, 1995, 20(3): 273. [11] Shevade S K, Keerthi S S, Bhattacharyya C, et al. IEEE Transactions on Neural Networks, 2002, 11(5): 1188. [12] MENG Geng-xiang，FANG Jing-long(蒙庚祥，方景龙). Computer Engineering and Design(计算机工程与设计), 2005，26(6): 1592.
