CONTINUOUS WAVELET ANALYSIS BASED SPECTRAL FEATURE SELECTION FOR WINTER WHEAT YELLOW RUST DETECTION
文献类型: 外文期刊
作者: Zhang Jingcheng 1 ; Luo Juhua 2 ; Huang Wenjiang 2 ; Wang Jihua 1 ;
作者机构: 1.Zhejiang Univ, Inst Agr Remote Sensing & Informat Syst Applicat, Hangzhou 310029, Zhejiang, Peoples R China
2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.Beijing Normal Univ, Sch Geog & Remote Sensing Sci, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
关键词: yellow rust;continuous wavelet analysis;linear discrimination analysis (LDA);quadratic discriminate analysis (QDA)
期刊名称:INTELLIGENT AUTOMATION AND SOFT COMPUTING ( 影响因子:1.647; 五年影响因子:1.469 )
ISSN: 1079-8587
年卷期: 2011 年 17 卷 5 期
页码:
收录情况: SCI
摘要: This study aims at identifying some mechanisms based on spectral features through continuous wavelet (CWT) analysis, and examining their estimating and discriminating power. In 2003, an inoculation of yellow rust fungal was conducted. Field measurements of canopy reflectance and biochemical properties were made with 5-7 day intervals during key growing period. Through a two-tailed paired student t-test, it was found that the variation of chlorophyll content is closely associated with the yellow rust infection. Based on this relationship, four spectral features were thus identified by CWT analysis. According to the results of stepwise linear regression and partial least squares (PLS) regression, the estimating power of those spectral features was not satisfied. However, the discriminating power of those features was revealed by linear discrimination analysis (LDA) and quadratic discriminate analysis (QDA), which yielded a success rate of 76.4%. Therefore, the CWT analysis and discriminate analysis are of great potential in identifying the yellow rust infected wheat plants.
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