Classification improvement based on non-white noise reduction using parafac decompositon for hyperspectral images

Abstract

The noise in the acquired hyperspectral image (HSI) is generally assumed as additive white Gaussian noise (WGN), while the estimation of the noise in each band of the real-world HSI shows that the noise is not white. Reducing the additive non-white noise is an important preprocessing step to further analyze the information in the HSIs by classification. A PMWF method, prewhitening and MWF (multidimensional Wiener filtering), was suggested and the non-white noise could be whitened by a pre-whitening procedure. While this method is time-consuming due to both the pre-whitening step and the estimation of three ranks of the MWF method. In this paper, we introduce a powerful multilinear algebra model, named parallel factor analysis (PARAFAC), which has only one rank and need not the pre-whitening procedure. To improve the classification, the rank of PARAFAC decomposition is estimated according to the maximum of the overall accuracy (OA) of the support vector machine (SVM) classification. The experiment results show that the PARAFAC model has high efficiency in the reduction of non-white noise and is a preferable preprocessing method for the accuracy improvement of the SVM classification.