对数变换与小波变换用于野外采集植物波谱降噪

FIELD COLLECTED PLANT SPECTRUM DENOISING BY LOGARITHM TRANSFORM AND WAVELET TRANSFORM

地物波谱野外测试过程中常引入噪声.本文结合植物波谱测试原理,提出波谱噪声属于乘性复合噪声.经理论推导,又提出了对数变换与小波变换相结合的降噪方法.仿真降噪试验结果表明,空域相关算法最适合于光谱数据降噪,模极大法次之,阈值法则不适于该类噪声的消减.对野外采集植物波谱的处理结果表明,空域相关去噪法对1450nm附近的噪声去除能力较强,1800-1900nm强噪声则去噪效果不理想.原因在于波谱仪纪录精度有限,当理论比值远大于1时,能够准确记录下来;远小于1时记录值为0,从而在强噪声干扰波段出现较严重的系统误差,经小波降噪后被视作奇异点被保留下来.研究表明对数变换与小波变换相结合采用空域相关去噪对于含乘性复合噪声的光谱是可行的.

The objects＇ spectrum is often contaminated by noise when it is collected in the open air. According to the principle of the spectrum collection, the noise was considered as one kind of multiplicative compound noise. By theoretical derivation, the combination of logarithm transform and wavelet transform was introduced into noise reduction. Multiplicative noise simulation test was carried out. And the results show that the spatial correlation algorithm is best suited for spectral data denoising, modulus maxima algorithm is inferior to it. Threshold shrinking rule is unsuitable for spectrum denoising. The wild plants spectrum were processed based on spatial correlation algorithm. Results show that the noise near 1450 nm in the spectrum is perfectly denoised, while near 1800 - 1900 nm strong noise can not be removed perfectly. The reason is the limited records accuracy of the spectrometer. When the theoretical ratio is far greater than 1, the spectrometer will accurately record them. While the theoretical ratio is far less than 1, the record will be 0. So serious system errors will be generated in strong noise band and will be retained after the wavelet transform was applied because they are considered as signal singularity. Experiments prove that spatial correlative filtering with the combination of logarithm transform and wavelet transform is feasible for multiplicative-noise-contaminated spectrum denoising.