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不同浓度铜离子(Cu2+)胁迫梯度下的玉米叶片光谱存在细微差异,但传统的光谱相似性测度方法难以区分健康的与受污染的玉米叶片光谱间的变异信息.为此,本文结合小波变换和奇异熵理论,构建小波奇异熵(Wavelet Singular Entropy,WSE)方法用于Cu2+胁迫下的玉米污染程度分析与污染监测.通过设置不同浓度Cu2+胁迫梯度下的玉米盆栽实验,依据玉米叶片的光谱测量数据和所测定的叶片中Cu2+含量,并与绿峰高度(Green-peak Height,GH)和红谷吸收深度(Red-valley Depth,RD)光谱特征参数法污染监测应用结果进行对比分析,研究WSE方法的Cu2+污染监测效果.结果表明,WSE值与Cu2+胁迫梯度、玉米叶片中Cu2+含量呈显著正相关,WSE值大小能够较好地表征叶片光谱的污染信息奇异性及其污染程度.
However, the traditional method of spectral similarity measure is difficult to distinguish the variation information of healthy and contaminated maize leaf spectra.Therefore, this paper combines wavelet transform and The singular entropy theory and the Wavelet Singular Entropy (WSE) method were used to analyze the degree of contamination and the pollution of corn under Cu2 + stress.According to the spectral data of maize leaves under different concentrations of Cu2 + stress gradient, And the content of Cu2 + in the leaves were determined and compared with the application results of the pollution monitoring of Green-peak Height (GH) and Red-valley Depth (RD) spectral parameters to study the WSE method The results showed that there was a significant positive correlation between WSE value and Cu2 + stress gradient and Cu2 + content in maize leaves. WSE value could better characterize the singularity and pollution degree of pollution information in leaf spectrum.