摘要
光谱维噪声使地物光谱扭曲或变形,中心波长偏移,影响地物信息提取和地表参量反演的精度。对光谱维噪声进行滤波处理,有利于改善遥感数据定量应用的效果。由于数学形态滤波的原理简单且较易实现,被应用到植被光谱以及有机化合物光谱的研究中。运用数学形态滤波对地面实测小麦光谱去噪,一方面对滤波后的光谱进行噪声和波形相似度的直观分析,另一方面通过植被指数反演小麦理化参量进行定量应用评价。结果表明,与传统Savitzky-Golay滤波相比,在可见-近红外波段范围内,数学形态滤波去噪后的光谱能够保持可见—近红外波段原始光谱的固有特征,叶面积指数和叶绿素的反演精度比去噪前有小幅提升,主要原因是实测光谱在该谱段范围的噪声影响很小;在短波红外波段范围内,数学形态滤波能有效去除短波红外大尺度噪声,提高叶片含水量的反演精度。而传统Savitzky-Golay滤波只能削弱短波红外大尺度噪声。广义形态滤波去噪后植被指数和叶片含水量之间的R2最高可达0.5130(去噪前0.3753),叶片含水量的反演值与实测值之间的R2最高可达0.4221(去噪前0.3097),RMSE为0.0243(去噪前0.0318),优于传统Savitzky-Golay滤波。
Noise in spectral dimension makes the spectrum distorted or deformed, shifting the central wave length, affecting the precision of fore,spectral denoising is of gre perspectral remote sensing.Beca to implement,it has been used i extracting lan at significance use the princip n the study of d cover information and inverting surface for improving the effects of quantitative le of mathematical morphological filtering vegetation spectra and fluorescence parameters. There applications of hy- is simple and easy spectra of organic compounds. Mathematical morphology filtering was used to remove spectral noise of wheat.Intuitive analysis was made according to the spectrum similarity and spectral denoising effects;Mo- reover, the quantitative evaluation in practical application was made by adopting different vegetation indices to invert the biophysical and biochemical parameters.The results show that,compared with the traditional Savitzky-Golay filter,the mathematical morphology filtering can keep the inherent characteristics in visible and near-infrared region,and improve the accuracy of inverting Leaf Area Index and Chlorophyll slightly, which is caused mainly by the low spectral noise in the range of the spectrum.The mathematical morpholo- gy filtering can remove the noise in shortwave infrared （SWIR） region effectively,improving the accuracy of inverting foliar water content of wheat. But the traditional Savitzky-Golay filter can only weaken the large scale noise in SWIR region.After generalized morphology filtering,the determination coefficient of re- gression （R2） between the vegetation index and foliar water content can reach 0.5130,while R2 is 0.3753 before filtering ;R2 between inverted value and the measurement of foliar water content can reach 0.4221 with a root mean square error （RMSE） of 0.0243 ,while the corresponding values are 0.3097 and 0.0318 for R2 and RMSE before filtering.The results of the mathematical morphology filter are better than those of the traditional Savitzky-Golay filter.
作者
张霞
戚文超
孙伟超
Zhang Xia Qi Wenchao Sun Weichao(Institute of Remote Sensing and Digital Earth ,Chinese Academy of Sciences , Beij ing 100101 ,China University of Chinese Academy of Sciences ,Beijing 100049,China)
出处
《遥感技术与应用》
CSCD
北大核心
2016年第5期846-854,863,共10页
Remote Sensing Technology and Application
基金
国家自然科学基金项目(41671360
40971205)
关键词
高光谱遥感
光谱维去噪
数学形态滤波
理化参量反演
Hyperspectral remote sensing
Spectral denoising
Mathematical morphology filtering
Physio-logical and biochemical parameters inversion
