土壤铅含量高光谱遥感反演中波段选择方法研究

A Study of Band Selection Method for Retrieving Soil Lead Content with Hyperspectral Remote Sensing Data

利用高光谱遥感数据进行了南京郊外土壤重金属元素铅的含量反演,由于高光谱数据波段众多,波段选择或变换至关重要。比较了基于次贪婪的前向选择模型的最小角度拟合和基于遗传算法进行波段选择的最小二乘和偏最小二乘拟合,结果发现基于遗传算法的偏最小二乘反演结果优于全波段的偏最小二乘,表明波段选择在高光谱反演重金属中是有益的。尽管采取了波段选择后的各方法在反演时均能达到70%以上的训练精度,但因遗传算法搜索的解空间范围更宽广,使得基于遗传算法的偏最小二乘优于前向选择模型的最小角度拟合。最后还比较了基于遗传算法的普通最小二乘和偏最小二乘拟合,结果表明偏最小二乘更优,因此在高光谱反演重金属含量当中,偏最小二乘精度较高,而在波段选择方法中,遗传算法更优。

To retrieve the lead content in the soil from hyperspectral remote sensing data,we should select bands or do some transformation first.In this paper,we compared Least Angle Regression,which is a modest forward choose method,and least squares regression and partial least squares regression based on genetic algorithm.As a validation result of the test area in Jianning,Nanjing,regression results with band selection are better than those without.Although Least Angle Regression,partial least squares regression with genetic algorithm can reach 70% training correctness,the latter based on genetic algorithm is better,because it can reach a larger solution space.At last,we conclude that partial least squares regression is a good choice for the lead content retrieval in soil by hyperspectral remote sensing data,and genetic algorithm can improve the retrieval by band selection promisingly.