对数变换与小波变换用于野外采集植物波谱降噪

周广柱; 王翠珍; 杨锋杰; 李寅明

红外与毫米波学报, 2009, 28 (4): 316, 网络出版: 2010-12-13

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

论文大纲

周广柱 ^1,*王翠珍 ¹杨锋杰 ²李寅明 ¹

作者单位

¹ 山东科技大学化学与环境工程学院, 山东青岛 266510

² 山东科技大学地质科学与工程学院, 山东青岛 266510

摘要

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.

Abstract

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

参考文献

[1] PU Rui-Liang, GONG Peng. Hyperspectral Remote Sensing and its Applications ［M］.Beijing: Higher Education Press (浦瑞良, 宫鹏. 高光谱遥感及其应用 .北京: 高等教育出版社), 2000, 82—85.

[2] XU Rui-Song, MA Yue-Liang, HE Zai-Cheng. Remote Sensing Applications on Biogeochemistry ［M］.Guangzhou: Guangdong Technology Press(徐瑞松, 马跃良, 何在成. 遥感生物地球化学. 广州: 广东科技出版社), 2003, 12— 90.

[3] Hatchell D C. Technical Guide, 4th ed. http://www.asdi. com/tg_rev4_wev4_web.pdf, Analytical spectral devices (ASD) Inc. USA, 1999, 18—24.

[4] PAN Quan, ZHANG Lei, MENG Jin-Li, et al. Wavelet Filtration and its Application ［M］. Beijing: Tsinghua University Press(潘泉, 张磊, 孟晋丽, 等. 小波滤波方法及应用. 北京: 清华大学出版社), 2005, 42—62.

[5] SUN Yan-kui. Wavelet Analysis and its Application ［M］. Beijing: China Machine Press(孙延奎. 小波分析及其应用. 北京:机械工业出版社), 2005, 219—221.

[6] Mallat S. A Wavelet Tour of Signal Processing (2nd Edition ) ［M］. Beijing: China Machine Press. 2003, 89—101.

[7] SHEN Yuan-Ting, NI Guo-Qiang, XU Da-Qi, et al. Study on gas exploration by hyperion hyperspectral remote sensing data［J］. J. Infrared Millim. Waves (沈渊婷, 倪国强, 徐大琦, 等.利用Hyperion短波红外高光谱数据勘探天然气的研究. 红外与毫米波学报 ), 2008, 27 (3):210—213

[8] ZHOU Feng-Qi, DI Xiao Guang, ZHOU Jun. Method of infrared image denoising based on stationary multiwavelet transform［J］. J. Infrared Millim. Waves (周凤岐, 小光, 周军.基于平稳多小波变换的红外图像噪声抑制方法. 红外与毫米波学报 ), 2005, 24 (2):151—155.

[9] ZHOU Guang-zhu, YANG Feng-jie, WANG Cui-zhen. Lifting transform via savitsky-golay filter predictor and application of denoising［J］. J. Of Coal Sci. & Engi (CHINA) ( 煤炭学报英文版 ), 2006, 12 (2):85—89.

[10] YANG Jian-Guo. Wavelet Analysis and Its Engineering Application ［M］. Beijing:China Machine Press(杨建国. 小波分析及其工程应用. 北京:机械工业出版社), 2005, 166—167.

[11] . Ideal spatial adaptation via wavelet shirinkage[J]. Biometrika, 1994, 81: 425.

[12] . A theory for multisolution signal decomposition: the wavelet representation[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674.

[13] . Singularity detect ion and processing with wavelet[J]. IEEE Trans on IT, 1992, 38(2): 617.

[14] Witkin A P. Scale-space filtering［A］.Proc. 8th Int. Joint Conf. Art. Intell［C］. 1983:1019—1022.

[15] . Wavelet transform filters: a spatially selective noise filtration technique[J]. IEEE Transforms on Image Processing, 1994, 3(6): 747.

[16] HU Guang-shu. Modern Signal Processing ［M］. Beijing: Tsinghua University Press(胡广书. 现代信号处理. 北京: 清华大学出版社), 2004, 390—405.

周广柱, 王翠珍, 杨锋杰, 李寅明. 对数变换与小波变换用于野外采集植物波谱降噪[J]. 红外与毫米波学报, 2009, 28(4): 316. ZHOU Guang-Zhu, WANG Cui-Zhen, YANG Feng-Jie, LI Yin-Ming. FIELD COLLECTED PLANT SPECTRUM DENOISING BY LOGARITHM TRANSFORM AND WAVELET TRANSFORM[J]. Journal of Infrared and Millimeter Waves, 2009, 28(4): 316.

对数变换与小波变换用于野外采集植物波谱降噪

关于本站 Cookie 的使用提示

全站搜索

对数变换与小波变换用于野外采集植物波谱降噪

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索