Hyperspectral Feature Band Selection Based on Mean Confidence Interval and Tree Species Discrimination
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 BaiMu, LeiZhu and WuHuanZi, the authors come up with two solutions through the theory of statistics; the first one is that optimal discriminating band between tree species is extracted by mean interval confidence, the other one is that tree species is discriminated by the Manhattan distance and the Min~Max interval similarity. The research results showed that (1) the optimal discriminating bands between BaiMu and LeiZhu are around 350~446, 497~527, 553~1 330, 1 355~2 400 and 2 436~2 500 nm; the optimal discriminating bands between BaiMu and WuHuanZi are around 434~555, 580~1 903, 1 914~2 089, 2 172~2 457 and 2 475~2 500 nm; the optimal discriminating bands between LeiZhu and WuHuanZi are around 434~555, 580~1 903, 1 914~2 089, 2 172~2 457 and 2 475~2 500 nm; and this result is helpful for us to find maximum difference to identifying tree species respectively. (2) In these optimal discriminating bands, we find that the Manhattan distance between the same species is far less than the different species; but the Min~Max interval similarity between the same species is far more than the different species, so this result could help us to discriminate and identify different types of tree species effectively.
陈永刚,丁丽霞,葛宏立,张茂震,胡 芸. 基于均值置信区间带的高光谱特征波段选择与树种识别[J]. 光谱学与光谱分析, 2011, 31(09): 2462-2466.
CHEN Yong-gang, DING Li-xia, GE Hong-li, ZHANG Mao-zhen, HU Yun. Hyperspectral Feature Band Selection Based on Mean Confidence Interval and Tree Species Discrimination . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(09): 2462-2466.
[1] SU Hong-jun,DU Pei-jun(苏红军,杜培军). Remote Sensing Technology and Application(遥感技术与应用), 2006, 21(4): 288. [2] De Backer S, Kempeneers P, Debruyn W, et al. Geoscience and Remote Sensing Letters, IEEE, 2005, 2(3): 319. [3] Guo B, Gunn S R, Damper R I, et al. Geoscience and Remote Sensing Letters, IEEE, 2006, 3(4): 522. [4] Martinez-Uso A, Pla F, Sotoca J M, et al. Geoscience and Remote Sensing, IEEE Transactions on, 2007, 45(12): 4158. [5] Clark M L, Roberts D A, Clark D B. Remote Sensing of Environment, 2005, 96(3-4): 375. [6] Gong P, Pu R, Biging G S, et al. Geoscience and Remote Sensing, IEEE Transactions on, 2003, 41(6): 1355. [7] Lipschutz S, Lipson M. Schaum’s Outline of Theory and Problems of Probability: Schau’s Outline Series, 2000. [8] Blum A L, Langley P. Artificial Intelligence, 1997, 97(1): 245. [9] SUN Hui-qin,XIONG Zhang(孙惠琴,熊 璋). Journal of Fudan University(复旦学报·自然科学版), 2004, 43(5): 819. [10] Faith D P, Minchin P R, Belbin L. Plant Ecology, 1987, 69(1): 57. [11] LI Jun-hua,PENG Li(李俊华,彭 力). Computer Engineering and Applications(计算机工程与应用), 2008, 44(2): 74. [12] Soman K P, Diwakar S, Ajay V. Insight into Data Mining: Theory and Practice: Prentice-Hall of India Pvt. Ltd., 2006.