为了消除高光谱遥感图像中光谱曲线的锯齿型噪声， 提高利用光谱曲线进行信息提取研究时的精度， 文章使用USGS（united states geological survey）光谱库中的植被光谱进行模拟， 添加了信噪比为30的噪声后采用小波阈值法进行噪声去除， 并利用信噪比、 均方误差和光谱角三项指标以及综合评估系数η来对去噪效果进行评估， 寻找出最佳的参数组合。 实验表明， 使用db12， db10， sym9， sym6等小波对含噪光谱进行3～7层分解， 采用软阈值函数处理小波变换系数并使用Heursure阈值方案进行阈值估计， 然后根据第一层小波分解的噪声水平估计进行阈值调整可以得到满意的去噪效果。 不过该方法对噪声水平有一定的依赖性， 针对不同噪声水平时需探索更合适的参数组合。

In order to remove the sawtoothed noise in the spectrum of hyperspectral remote sensing and improve the accuracy of information extraction using spectrum in the present research, the spectrum of vegetation in the USGS (United States Geological Survey) spectrum library was used to simulate the performance of wavelet denoising. These spectra were measured by a custom-modified and computer-controlled Beckman spectrometer at the USGS Denver Spectroscopy Lab. The wavelength accuracy is about 5 nm in the NIR and 2 nm in the visible. In the experiment, noise with signal to noise ratio (SNR) 30 was first added to the spectrum, and then removed by the wavelet denoising approach. For the purpose of finding the optimal parameters combinations, the SNR, mean squared error (MSE), spectral angle (SA) and integrated evaluation coefficient η were used to evaluate the approach’s denoising effects. Denoising effect is directly proportional to SNR, and inversely proportional to MSE, SA and the integrated evaluation coefficient η. Denoising results show that the sawtoothed noise in noisy spectrum was basically eliminated, and the denoised spectrum basically coincides with the original spectrum, maintaining a good spectral characteristic of the curve. Evaluation results show that the optimal denoising can be achieved by firstly decomposing the noisy spectrum into 3-7 levels using db12, db10, sym9 and sym6 wavelets, then processing the wavelet transform coefficients by soft-threshold functions, and finally estimating the thresholds by heursure threshold selection rule and rescaling using a single estimation of level noise based on first-level coefficients. However, this approach depends on the noise level, which means that for different noise level the optimal parameters combination is also diverse.