CEM的波段选择方法研究及应用

Research and Application of Band Selection Method Based on CEM

高光谱数据信息量丰富,波段数量多,能够为地物分析提供更全面的依据,但同时也增加了数据分析的复杂性和干扰性,尤其是水质遥感监测等低信噪比的应用领域。传统波段选择常借助相关性系数等方法,在众多光谱波段中选择标识波段,并在所选波段集合上进行数据分析。基于约束能量最小化(CEM)从信号检测角度提出了一种面向目标向量的波段选择算法——基于CEM的波段选择算法(CBS),采用信号匹配滤波器从观测向量中找出与目标向量匹配度高的波段,结合正交原理,最大程度地选出与目标向量匹配度高且波段向量冗余度低的波段子集。以水质监测中的成分测定作为验证,采集辽河入海口试验区的高光谱数据,结合同步实地水样数据进行建模,预测辽河水域氮磷含量。比较CBS算法的波段选择结果和皮尔逊相关系数(PCC)波段选择结果,将两种方法得到的显著性波段子集作为变量进行逐步回归分析,建立多元回归模型,进一步对模型进行精度检验,分析其预测值与真实值的平均相对误差。总磷浓度模型的精度检验中,通过PCC算法选择波段得到的模型平均相对误差为20.7%,而通过CBS算法选择波段得到的模型平均相对误差为8.17%;总氮浓度模型的精度检验中,通过PCC算法选择波段得到的模型平均相对误差为16.8%,而通过CBS算法选择波段得到的模型平均相对误差为12.4%。数据分析的结果表明, CBS算法得到的波段子集,在氮磷浓度反演的能力上,优于传统基于相关系数的选择方法。

Hyperspectral data is rich in information and bands,which can provide a more comprehensive basis for geophysical analysis,but at the same time,it also increases the complexity and interference of data analysis,especially in low signal-to-noise ratio applications such as remote sensing monitoring of water quality.Traditional band selection often uses correlation coefficient and other methods to select the identification band in many spectral bands and to analyze the data on the selected band set.In this paper,based on the constrained energy minimization(CEM),a target-oriented band selection algorithm is proposed,which is called CEM-based band selection(CBS).The signal matching filter is used to find the band with a high matching degree with the target vector from the observation vector,and then combined with the orthogonal principle to maximize the selection of a subset of bands that have a high degree of matching with the target vector and low redundancy of the band vector.Based on the determination of the components in the water quality monitoring,the hyperspectral data of the Liaohe estuary test area was collected and combined with the synchronous field water sample data to predict the nitrogen and phosphorus content in the Liaohe waters.Comparing the band selection results of the CBS algorithm with the band selection results of the Pearson correlation coefficient(PCC),the significant band subsets obtained by the two methods are used as variables to carry out stepwise regression analysis,and multiple regression models are established to further test the accuracy of the model and analyze the average relative error between the predicted value and the true value.In the accuracy test of the total phosphorus concentration model,the average relative error of the model obtained by the PCC algorithm is 20.7%,and the average relative error of the model obtained by the CBS algorithm is 8.17%.In the accuracy test of the total nitrogen concentration model,the average relative error of the model obtained by the PCC algorithm is 16.8%,and the average relative error of the model obtained by the CBS algorithm is 12.4%.The results of the data analysis show that the band subset obtained by the CBS algorithm is superior to the traditional selection method based correlation coefficient in the ability of nitrogen and phosphorus concentration inversion.