A new method that combines spectral indexes and Naive Bayes to distinguish heavy metal pollution in crops. Issue 7 (3rd July 2021)

• Record Type: Journal Article
• Authors: Li, Yanru; Yang, Keming; Han, Qianqian; Gao, Wei; Zhang, Jianhong; Wu, Bing
• Is Part Of: Remote sensing letters. Volume 12:Issue 7(2021)
• Page Start: 666
• Page End: 673
• Publication Date: 2021-07-03
• Subjects: Remote sensing -- Periodicals; Remote sensing; Periodicals; 621.3678
• DOI: 10.1080/2150704X.2021.1910364
• Languages: English
• ISSNs: 2150-704X

Abstract

ABSTRACT: Applying machine learning to hyperspectral remote sensing is a new way to solve practical problems. A new method that combines spectral indexes and Naive Bayes, named SINB, was proposed to distinguish heavy metal pollution in crops. First, the CLPIOR, CLPICR, and CLPIFOD (Copper-Lead Pollution Index of the original spectrum, continuum removed spectrum, and first-order differential spectrum) were constructed based on the processed crop spectra, and they were used as input variables for Bayes discrimination. Then, the discriminant functions were constructed using samples from the training group, and the discriminant rule was formulated. Finally, the samples from the validation group were used to test the universality and robustness of the SINB. The results showed that the CLPIOR, CLPICR, and CLPIFOD were sensitive to distinguish Cu (Copper) and Pb (Lead) pollution, and the discrimination accuracy of SINB was 100% in the training group and 100% in the validation group. This method fully used the crop leaf spectral characteristics and the advantages of machine learning and achieved the discrimination of crop heavy metal pollution.