PROJECT TITLE :

Tensor decomposition and PCA jointed algorithm For hyperspectral image denoising - 2016

ABSTRACT:

Denoising is a important preprocessing step for hyperspectral image (HSI) classification and detection. Ancient ways sometimes convert high-dimensional HSI knowledge to a pair of-D knowledge and method them separately. Consequently, the inherent structured high-dimensional data in the original observations could be discarded. To overcome this disadvantage, this letter tackles an HSI denoising by jointly exploiting Tucker decomposition and principal part analysis (PCA). A truncated Tucker decomposition technique primarily based on noise power ratio (NPR) analysis and jointed with PCA is presented. We tend to call this jointed technique as NPR-Tucker+PCA. Experimental results show that the proposed methodology outperforms existing strategies within the sense of peak signal-to-noise ratio performance.


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Matrix Decomposition Hyperspectral Imaging Image Denoising Image Classification Principal Component Analysis
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