The present study introduced the generalized morphological filter intothe denoising of visible and near infrared spectra for the first time, andprovided a new method for denoising the reflectance spectra by combiningmathematical morphology methods with the wavelet packet transformation. Theauthors used vegetable spectra from USGS spectral library as the referencespectra, and obtained the noised spectra by adding noises with different signal-to-noise ratios to the referenced spectra. The results were evaluated by signal-to-noise ratio (SNR), root mean squared error (RMSE), normalized correlationcoefficient (NCC) and smoothness ratio (SR) of the denoised spectra. The authors’ results showed that both the thresholding on wavelet packet decomposition bestbases method and the generalized morphological filter method could maintain thespectral shape and the spectral smoothness after denoising. The generalizedmorphological filter method can remove larger amplitude random noise whereas thecontinuous small amplitude random noise could not be removed well. Hence, thedenoised spectra were not smooth. Nevertheless, the denoised spectra using thethresholding on the best base groups of wavelet packet decomposition method weresmooth, but the larger amplitude noise could not be removed completely. Theauthors’ method by combining the two methods has the merits of the two methodsbut removing their defects. The results showed that both large and smallamplitude noise could be removed completely, meanwhile the normalized correlationcoefficient (NCC) and smoothness ratio (SR) were improved, which indicated thatthe authors’ method is superior to other methods in denoising visible and nearinfrared spectra.morphological filter; Thresholding on wavelet packet decomposition best bases与中国科学院创新项目(Kzcx2-yw-107)联合资助