Classification and Recognition of Freshwater Phytoplankton Based on Multi-source Fluorescence Spectrum Data Fusion
In the process of classification and recognition of freshwater phytoplankton,single fluorescence spectrum data was mainly used to extract features,and the feature information obtained was relatively one-sided,which made the F1 score of classification and recognition results low.Therefore,on the premise of multi-source fluorescence spectrum data fusion,a new classification and recognition method of freshwater phy-toplankton was proposed.The local linear embedding algorithm was used to reduce the dimension of multi-source fluorescence spectrum data,and then the wavelet decomposition algorithm was used to extract the spec-tral feature information.The independent component analysis algorithm was used to mark the effective feature information.Based on the multi-source fluorescence spectrum data fusion principle,the multi-source effec-tive spectral features were fused to output comprehensive phytoplankton spectral feature information.The spectral features was input into the support vector machine model to solve the multi classification problem,and generate the classification and recognition results of freshwater phytoplankton.The experimental results showed that the F1 score of the proposed method was 0.95 when the noise ratio was 40%,which was 14.74%and 18.95%higher than the other two methods,and the classification results were more accurate.
freshwater phytoplanktonclassification and recognition methodsmulti-source fluorescence spectrumdata fusionfeature extractionwavelet decomposition
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