In hyperspectral image (HSI) analysis, dimensionality reduction is a preprocessing step for HSI classification. Independent component analysis (ICA) reduces the spectral dimension and does not utilize the spatial information of the HSI. To solve it, tensor decompositions have been successfully applied to joint noise reduction in spatial and spectral dimensions of HSIs, such as parallel factor analysis (PARAFAC). However, the PARAFAC method does not reduce the dimension in the spectral dimension. We proposed a method to improve it, which combines ICA and PARAFAC to reduce both the dimension in the spectral dimension and the noise in the spatial and spectral dimensions. The experimental results indicate that this method improves the classification compared with the previous methods.
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.
Dimensionality reduction is a preprocessing step for hyperspectral image (HSI) classification. Principal component analysis reduces the spectral dimension and does not utilize the spatial information of an HSI. Both spatial and spectral information are used when an HSI is modeled as a tensor, that is, the noise in the spatial dimension is decreased and the dimension in a spectral dimension is reduced simultaneously. However, this model does not consider factors affecting the spectral signatures of ground objects. This means that further improving classification is very difficult. The authors propose that the spectral signatures of ground objects are the composite result of multiple factors, such as illumination, mixture, atmospheric scattering and radiation, and so on. In addition, these factors are very difficult to distinguish. Therefore, these factors are synthesized as within-class factors. Within-class factors, class factors, and pixels are selected to model a third-order tensor. Experimental results indicate that the classification accuracy of the new method is higher than that of the previous methods.
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