利用独立成分分析的高光谱图像波段选择方法

Independent component analysis-based band selection for hyperspectral imagery

提出一种适合目标探测的基于独立成分分析(ICA)的高光谱图像波段选择方法。首先进行"虚拟维"(VD)估计以确定重要独立成分个数,同时对FastICA生成的独立成分排序,选择排序靠前的几个独立成分作为重要独立成分;再根据波段对重要独立成分的平均贡献量对波段排序;最后使用光谱相似性度量去除排序后的冗余波段,保证了最终波段子集含有较多的目标信息。对AVIRIS获取的两幅真实高光谱图像进行了目标探测实验,结果表明,文中方法优于另外两种基于二阶统计特性的波段选择方法,其选出的波段分别占据全部波段的12%和3%,目标探测算子自适应余弦估计(ACE)和自适应匹配滤波(AMF)其上的探测率较全波段分别提高了30%和15%。

An independent component analysis（ICA）-based band selection method suitable for target detection in hyperspectral imagery was proposed.Firstly,the number of important independent components were obtained by estimating virtual dimensionality（VD） value while prioritizing the independent components generated by FastICA.After all the bands were ranked according to their average contributions to the important independent components,a spectral similarity measure was used for band decorrelation to remove the redundant bands and make the final selected bands contain the most target information.Two real AVIRIS hyperspectral data sets were tested for target detection.As shown in the experimental results,the proposed method outperforms the other two existing second-order statistics-based band selection methods,and it selects less than 12% and 3% of the full bands respectively,with which the detection probability of adaptive cosine estimator（ACE） and adaptive matched filter（AMF） has been improved by 30% and 15%.