Hyperion高光谱影像波段选择方法比较研究

Hyperion影像的光谱分辨率高，数据体积庞大，而且相邻波段之间的相关性强，信息冗余度较高， 给数据处理与解译带来了很多问题。鉴于此，提出了通过将分段主成分分析和波段指数相结合来开展波段选择与降维研究的思想。 同时采用自适应波段选择法、波段指数法和主成分分析累计贡献率方法进行了波段选择方法的对比研究；对4种波段选择方法所得到的结 果进行了最佳波段组合、地物可分性和图像变换比较分析。实验结果表明，分段主成分分析与波段指数综合方法可以有效抑制由于全局变换造成局部重要光谱被滤除的现象 ，同时还可兼顾自适应分区后各子区间及区间内波段之间的相关性，有效降低高光谱数据的维度。由此可见，该方法的波段选择效 果优于传统的自适应波段选择方法、波段指数法以及主成分分析累计贡献率方法。

Comparison of Band Selection Methods for Hyperion Image Data

Hyperion images have the features of high spectral resolution , large quantity of data, strong correlation among adjacent bands and high information redundancy. These features have brought many problems to the data processing and interpretation of them. To solve these problems, an idea of band selection and dimensionality reduction by combining the segmented principal component analysis with the band index algorithm is proposed. The adaptive band selection method, band index method and cumulative contribution rate method are used to carry out a comparative study in band selection. The results obtained by four band selection methods are analyzed in optimal band combination, feature reparability and image transformation. The experimental results show that the segmented principal component analysis and band index algorithm can not only restrain the phenomenon that some local important spectra may be filtered due to the global transformation, but also can take into account the adaptive partition subinterval correlation among the bands and effectively reduce the hyperspectral data dimension. The method is better than the traditional adaptive band selection method, band index method and cumulative contribution rate method in band selection.