首页|离散小波去噪后冬小麦叶片含水量高光谱估算

离散小波去噪后冬小麦叶片含水量高光谱估算

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
光谱噪声去除是遥感区域应用的必要过程,噪声去除效果能直接影响区域地表信息的监测精度.为分析离散小波算法对光谱数据的分解机理,探寻基于离散小波算法光谱噪声信息去除与光谱处理方法,以冬小麦冠层光谱与叶片含水量为数据源,先利用离散小波算法对光谱数据进行去噪处理,采用的小波基为Meyer;然后以Meyer、Sym2、Coif2为小波基对去噪后的光谱数据进行信息分离,并结合相关性分析算法、偏最小二乘算法构建冬小麦叶片含水量估测模型,研究结论如下:(1)在离散小波算法下,合并的光谱曲线随合并尺度数的不断增加,原光谱曲线局部的大、中、小特征依次凸显;随H10-H1分解尺度的依次加入,分解信息对合并曲线的修正幅度也逐步减弱,其中,将H3-H1依次合并后,合并的光谱曲线几乎无变动.(2)提出的去噪方法可在一定程度上改变了部分光谱对冬小麦叶片含水量的敏感性及敏感波段的分布:其中在1～3尺度内,降低了光谱对冬小麦叶片含水量的敏感性,改变了敏感波段的波段位置的分布情况.在4～10尺度内,能明显提升光谱对冬小麦叶片含水量的敏感性(Coif2);提出的去噪方法可提升局部波段对冬小麦叶片含水量的敏感性(Sym2).(3)提出的去噪方法能明显提升光谱对模型的稳定性,能提升Sym2、Coif2小波基内最优模型的精度与稳定性,其中验证精度提高了 8.6％(Sym2)、34.1％(Coif2),表明该研究提出的去噪处理是有效的.
Study on Quantitative Inversion of Leaf Water Content of Winter Wheat Based on Discrete Wavelet Technique
Spectral noise removal is a necessary process for remote sensing regional applications,and the noise removal effect can directly affect the monitoring accuracy of regional surface information.To study and analyze the decomposition mechanism of the discrete wavelet algorithm on spectral data and explore the spectral noise information removal and spectral processing method based on the discrete wavelet algorithm,this study takes the winter wheat canopy spectra and leaf water content as the data source,and then denoises the spectral data using the discrete wavelet algorithm with the wavelet base of Meyer;and then separates the information of the denoised spectral data by using the wavelet bases of Meyer,Sym2,and Coif2,and constructs the spectral data by combining the correlation analysis algorithm and partial least squares algorithm.Then,we separated the information of the denoised spectral data with Meyer,Sym2,and Coif2 as the wavelet bases and constructed a model for estimating the water content of winter wheat leaves by combining the correlation analysis algorithm and partial least squares algorithm.The study's conclusions are as follows:(1)Under the discrete wavelet algorithm,with the increasing number of merging scales of the merging spectral curves,the original spectral curves'local large,medium,and small features were highlighted in order.The correction amplitude of the merging curves was also gradually reduced with the joining of the decomposition scales of H10-H1.With the sequential addition of H10-H1 decomposition scales,the magnitude of the correction of the decomposition information to the merged curves is also gradually weakened,in which the merged spectral curves are almost unchanged after the sequential merging of H3-H1.(2)The denoising method proposed in this paper can change the sensitivity of the spectra to the water content of winter wheat leaves and the band positions of the sensitive bands to a certain extent:in the 1～3 scale,the sensitivity of the spectra to the water content of winter wheat leaves is reduced,and the distribution of the band positions of the sensitive bands is changed.Within 4～10 scales,it can significantly enhance the sensitivity of the spectrum to the water content of winter wheat leaves(Coif2);the denoising method proposed in the study can enhance the sensitivity of the local bands to the water content of winter wheat leaves(Sym2).(3)The denoising method proposed in this study can significantly improve the stability of the spectrum to the model.It can improve the accuracy and stability of the optimal model within the Sym2 and Coif2 wavelet bases,in which the validation accuracy is improved by 8.6％(Sym2)and 34.1％(Coif2),which indicates that the denoising treatment proposed in this study is effective.

Winter wheatLeaf water contentDiscrete waveletNoise informationHyperspectral

王延仓、朱玉晨、齐焱鑫、张志通、曹会琼、王金杲、顾晓鹤、唐瑞尹、何跃君、李笑芳、罗巍

展开 >

北华航天工业学院遥感信息工程学院,河北廊坊 065000

北京农林科学院信息技术研究中心,北京 100097

航天遥感信息应用技术国家地方联合工程研究中心,河北廊坊 065000

河北省航天遥感信息处理与应用协同创新中心,河北廊坊 065000

中国地质科学院水文地质环境地质研究所,河北石家庄 050061

廊坊师范学院,河北廊坊 065000

展开 >

冬小麦 叶片含水量 离散小波 噪声信息 高光谱

河北省教育厅科学技术研究项目国家重点研发计划项目高分辨率对地观测系统重大专项河北省教育厅研究项目2023年大学生创新训练计划项目2024年大学生创新训练计划项目2024年度校级科研项目

QN20192132016YFD030060930-Y30F06-9003-20/22ZC2023089S202310100008S2024101000012JYB201419

2024

10.3964/j.issn.1000-0593(2024)09-2559-09

光谱学与光谱分析
中国光学学会

光谱学与光谱分析

CSTPCD北大核心
影响因子：0.897
ISSN：1000-0593
年,卷(期)：2024.44(9)