空间光学技术研究室知识库

In hyperspectral image analysis, classification requires spectral dimensionality reduction (DR). Tensor decompositions have been successfully applied to joint noise reduction in spatial and spectral dimensions of hyperspectral images, such as parallel factor analysis (PARAFAC). However, the PARAFAC method does not reduce the dimension in the spectral dimension. To improve itï1/4a new method was proposed in this paper, that is, combine PCA and PARAFAC to reduce both the dimension in the spectral dimension and the noise in the spatial and spectral dimensions. The experiment results indicate that the new method improves the classification compared with the previous methods. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.