Denoising and dimensionality reduction based on PARAFAC decomposition for hyperspectral images

Rong-hua Yan; Jin-ye Peng; De-sheng Wen; Dong-mei Ma

doi:10.1117/12.2505370

12 December 2018 Denoising and dimensionality reduction based on PARAFAC decomposition for hyperspectral images

Rong-hua Yan, Jin-ye Peng, De-sheng Wen, Dong-mei Ma

Author Affiliations +

Proceedings Volume 10846, Optical Sensing and Imaging Technologies and Applications; 1084624 (2018) https://doi.org/10.1117/12.2505370
Event: International Symposium on Optoelectronic Technology and Application 2018, 2018, Beijing, China

Abstract

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 it，a 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.

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Rong-hua Yan, Jin-ye Peng, De-sheng Wen, and Dong-mei Ma "Denoising and dimensionality reduction based on PARAFAC decomposition for hyperspectral images", Proc. SPIE 10846, Optical Sensing and Imaging Technologies and Applications, 1084624 (12 December 2018); https://doi.org/10.1117/12.2505370

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