Unsupervised linear spectral mixture analysis with AVIRIS data

摘要

A new algorithm for unsupervised hyperspectral data unmixing is investigated,which includes a mod-"ified minimum noise fraction(MNF)transformation and independent component analysis(ICA).The modified"MNF transformation is used to reduce noise and remove correlation between neighboring bands.Then the ICA is"applied to unmix hyperspectral images,and independent endmembers are obtained from unmixed images by u-"sing post-processing which includes image segmentation based on statistical histograms and morphological opera-"tions.The experimental results demonstrate that this algorithm can identify endmembers resident in mixed pix-"els.Meanwhile,the results show the high computational efficiency of the modified MNF transformation.The"time consumed by the modified method is almost one fifth of the traditional MNF transformation."