Tensor decomposition and PCA jointed algorithm For hyperspectral image denoising - 2016 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. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Matrix Decomposition Hyperspectral Imaging Image Denoising Image Classification Principal Component Analysis sequence-to-sequence similarity-based filter for image denoising - 2016 A novel lip descriptor for audio-visual keyword Spotting based on adaptive decision fusion - 2016
