Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data
Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data
复制标题
高光谱数据检测小麦黄锈病光谱间隔对小波特征的影响
DOI：
10.25165/ijabe.v11i6.4168
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发表时间：
2018-12
期刊：
影响因子：
--
通讯作者：
Wenjiang Huang
中科院分区：
文献类型：
--
作者：
Jingcheng Zhang;Bin Wang;Xuexue Zhang;Peng Liu;Yingying Dong;Kaihua Wu;Wenjiang Huang
Detection of yellow rust using hyperspectral data is of practical importance for disease control and prevention. As an emerging spectral analysis method, continuous wavelet analysis (CWA) has shown great potential for the detection of plant diseases and insects. Given the spectral interval of airborne or spaceborne hyperspectral sensor data differ greatly, it is important to understand the impact of spectral interval on the performance of CWA in detecting yellow rust in winter wheat. A field experiment was conducted which obtained spectral measurements of both healthy and disease-infected plants. The impacts of the mother wavelet type and spectral interval on disease detection were analyzed. The results showed that spectral features derived from all four mother wavelet types exhibited sufficient sensitivity to the occurrence of yellow rust. The Mexh wavelet slightly outperformed the others in estimating disease severity. Although the detecting accuracy generally declined with decreasing of spectral interval, relatively high accuracy levels were maintained (R2>0.7) until a spectral interval of 16 nm. Therefore, it is recommended that the spectral interval of hyperspectral data should be no larger than 16 nm for the detection of yellow rust. The relatively loose spectral interval requirement permits extensive applications for disease detection with hyperspectral imagery.
Keywords: continuous wavelet analysis, spectral interval, hyperspectral data, wheat yellow rust
DOI: 10.25165/j.ijabe.20181106.4168
Citation: Zhang J C, Wang B, Zhang X X, Liu P, Dong Y Y, Wu K H, et al. Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data. Int J Agric & Biol Eng, 2018; 11(6): 138–144.
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