English

应用科学学报 ›› 2012, Vol. 30 ›› Issue (1): 105-110.doi: 10.3969/j.issn.0255-8297.2012.01.016

• 信号与信息处理 • 上一篇    

利用支持向量机构建水稻镉含量高光谱预测模型

吕杰， 刘湘南   

  1. 中国地质大学（北京）信息工程学院，北京100083
  • 收稿日期:2011-05-01 修回日期:2011-06-28 出版日期:2012-02-09 发布日期:2012-01-30
  • 作者简介:吕杰，博士生，研究方向：空间信息智能处理，E-mail: jasonlv168@gmail.com；刘湘南，教授，博导，研究方向：资源环境遥感信息机理、自然灾害遥感模型，E-mail: liuxncugb@163.com
  • 基金资助:

    国家自然科学基金（No.40771155）；国家“863”高技术研究发展计划基金（No.2007AA12Z174）资助

Hyperspectral Remote Sensing Estimation Model for Cd Concentration in Rice Using Support Vector Machines

LÜ Jie, LIU Xiang-nan   

  1. School of Information Engineering, China University of Geosciences, Beijing 100083, China
  • Received:2011-05-01 Revised:2011-06-28 Online:2012-02-09 Published:2012-01-30

PDF

1454

被引次数

9

摘要：

 研究基于支持向量机构建水稻镉（Cd）含量高光谱预测模型的可行性. 利用ASD光谱仪测量研究区水稻冠层反射光谱，通过实验室化学分析得到土壤镉含量和水稻叶片镉含量，对研究区水稻光谱进行均一化平滑处理以及小波变换降噪，构建基于支持向量机（support vector machines, SVM）的水稻镉含量高光谱预测模型. 结果表明，小波变换降噪处理对SVM建立的镉含量预测模型精度有所提高，SVM高光谱预测模型的相关系数为0.867 4, 均方误差为0.001 2. 该研究为利用高光谱遥感大面积、快速监测农田作物重金属污染提供技术支持.

关键词: 水稻, 镉, 高光谱, 小波变换, 支持向量机

Abstract:

Research was carried out to explore possibility of using support vector machines (SVM) to estimate Cd concentration from hyperspectral reflectance. Canopy spectral measurements from rice plants were collected using an ASD field spectrometer in the experiment sites. Soil samples and rice samples were collected
for chemical analysis of Cd concentrations. A normalization spectral pre-processing method was employed to improve performance of the estimation model. Wavelet transforms were adopted to denoise the rice hyperspectral. Estimation of Cd concentration was achieved by an SVM approach. Compared to the original
(undenoised) hyperspectrl estimation model, the SVM model based on wavelet transforms yielded promising results with a coefficient of determination of 0.867 4 and a mean square error (MSE) of 0.001 2. The results indicate that it is possible to estimate Cd concentration in rice using wavelet transforms and SVM. This study can provide technical support for large area monitoring of heavy metals stressed crops using hyperspectral remote sensing.

Key words: rice, Cd, hyperspectral, wavelet transform, support vector machines (SVM)

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