[Reduction of hyperspectral dimensions and construction of discriminating models for identifying wetland plant species].

The present paper researched and analyzed the hyperspectral data of wetland plant species often occurred in Beijing. The methods of Mahalanobis Distance (MD) and principal component analysis (PCA) were mainly applied to reduce the dimensions of hyperspectral data and to analyze and extract the features of spectra. The authors use the extracted spectra to build identification models for identifying the wetland species. The authors then compared and evaluated the precisions of models and finally obtained the best discriminating model. The results showed that (1) the dimensions of hyperspectral data can be efficiently reduced by both MD and PCA methods. (2) The discriminating models established using the parameters extracted from the resulting spectra of MD and PCA could identify the wetland plants with high precisions of more than 90%. As a result, the conversion and usage of the hyperspectral data can help better understand and well extract the spectra of different wetland plants. Furthermore, the constructed discriminating models for wetland species could also be used in the future to guide us in mapping and monitoring of wetland ecosystem by applying the remote sensing data.