主成分分析在高光谱遥感图像降维中的应用

THE PRINCIPAL COMPONENT ANALYSIS APPLIED TO HYPERSPECTRAL REMOTE SENSING IMAGE DIMENSIONAL REDUCTION

高光谱遥感图像的高数据维给图像进一步处理带来了困难,为了解决这一问题,本文提出了主成分分析的降维方法.根据原始数据协方差阵的特征值和特征向量,可以计算各个波段对给定主成分的贡献率,对重要主成分贡献率的和直接反应了波段信息量的大小,实验证明,该方法效果较好,且计算量小.

The high dimensions of hyperspectral remote sensing image have brought problems to further processing.In order to solve the above problems,this paper propose the Principal Component Analysis algorithm of dimensional reduction.With eigenvalues and eigenvectors of the covariance metrics of the original data,the contribution of a given band to a certain principle component can be calculated,the sum of the contribution of a given band to all important principle components can reflect the information of the band,the results of the experiments indicate that the algorithm is validate and needs little computation.