Research on Wavelet Analysis in the Characteristics Extracting and Identification of Discrete 3D Fluorescence Spectra of Phytoplankton
Research on Wavelet Analysis in the Characteristics Extracting and Identification of Discrete 3D Fluorescence Spectra of Phytoplankton
复制标题
DOI：
--
复制
发表时间：
2007
影响因子：
--
通讯作者：
Zhu Chen-jian
中科院分区：
文献类型：
--
作者：
Zhu Chen-jian
为了区分和鉴别不同属的浮游植物，选择Daubechies-3的二次尺度函数对10种浮游植物进行特征提取。贝叶斯判别分析结果表明，这些特征光谱在属水平上的识别率为96.75%。从这些特征光谱中得到了标准光谱。非负最小二乘法的结果表明，它们可以定性地识别加入不同比例随机噪声的单个物种，识别率为100%。大多数浮游植物混合体的优势属(种)可以通过它们进行定性鉴定。有些物种的识别量可以达到真实物种的75%以上。结果表明，小波分析是从属水平上提取浮游植物特征的有效方法。
In order to discriminate and identify phytoplankton of different genuses, the second scaling function of Daubechies-3 was selected to extract the characteristics of 10 phytoplankton species. The results of Bayes discriminant analysis show that these characteristic spectra show a discriminating rate of 96.75% at genus level. Standard spectra were obtained from these characteristic spectra. The results of the nonnegative least square show that they can identify qualitatively some single species added with different ratios of random noise with a ratio of 100%. The dominant genus (species) of most phytoplankton mixtures can be identified by them qualitatively. The identifying amount of some species can be more than 75% of the real one. Wavelet analysis was proven to be an effective method in extracting the characteristic features of phytoplankton on the level of genus.