title: [Hyperspectral feature band selection based on mean confidence interval and tree species discrimination].
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.
authors.affiliation: 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. cyg_gis@163.com
authors.forename: Yong-Gang
authors.initials: YG
authors.lastname: Chen
authors.forename: Li-Xia
authors.initials: LX
authors.lastname: Ding
authors.forename: Hong-Li
authors.initials: HL
authors.lastname: Ge
authors.forename: Mao-Zhen
authors.initials: MZ
authors.lastname: Zhang
authors.forename: Yun
authors.initials: Y
authors.lastname: Hu
mesh_terms: D016001:Confidence Intervals; D018515:Plant Leaves; D013057:Spectrum Analysis; D014197:Trees / Q000145:classification*