Species Identification of Wetland Vegetation: Based on Machine Learning and Hyperspcetral Data

doi:10.11924/j.issn.1000-6850.casb18110118

Abstract

Abstract: The paper aims to explore the species identification of wetland vegetation by using hyperspectral and high spatial resolution remote sensing data. Based on the analysis of the original reflection spectra, secondorder differential spectra and continuum removal spectra of 6 wetland vegetation species, we extracted the characteristic bands by the methods of Mahalanobis distance and correlation coefficient, and took it as a characteristic parameter to participate in the classification and information extraction of C5.0 decision tree. The results showed that: (1) the overall classification accuracy of C5.0 decision tree based on machine learning was 79.87% , and the Kappa coefficient was 0.765; compared with the supervised classification maximum likelihood method, the overall accuracy of vegetation information extraction increased by 9.95% , and the Kappa coefficient increased by 0.114; (2) compared with the maximum likelihood method, the C5.0 decision tree method based on machine learning had the unique advantage that the information extraction precision of algae was greatly improved, and the user precision of watermifoil and Egeria increased the most, by 18.67% and 15.86%, respectively. The method can achieve high-precision extraction of wetland vegetation species information, provide reference for similar research, and supply scientific and technical support for wetland ecological health assessment.

CLC Number:

P237

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