Tensor Decomposition and PCA Jointed Algorithm for Hyperspectral Image Denoising

Denoising is a critical preprocessing step for hyperspectral image (HSI) classification and detection. Traditional methods usually convert high-dimensional HSI data to 2-D data and process them separately. Consequently, the inherent structured high-dimensional information in the original observations may be discarded. To overcome this disadvantage, this letter tackles an HSI denoising by jointly exploiting Tucker decomposition and principal component analysis (PCA). A truncated Tucker decomposition method based on noise power ratio (NPR) analysis and jointed with PCA is presented. We call this jointed method as NPR-Tucker+PCA. Experimental results show that the proposed method outperforms existing methods in the sense of peak signal-to-noise ratio performance.

[1]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[2]  Lei Zhang,et al.  Band-Subset-Based Clustering and Fusion for Hyperspectral Imagery Classification , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Liangpei Zhang,et al.  Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Begüm Demir,et al.  Hyperspectral Image Classification Using Denoising of Intrinsic Mode Functions , 2011, IEEE Geoscience and Remote Sensing Letters.

[5]  Antonio J. Plaza,et al.  Subspace-Based Support Vector Machines for Hyperspectral Image Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[6]  Kang Sun,et al.  A New Sparsity-Based Band Selection Method for Target Detection of Hyperspectral Image , 2015, IEEE Geoscience and Remote Sensing Letters.

[7]  Peter M. Atkinson,et al.  A Multiple-Mapping Kernel for Hyperspectral Image Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[8]  José M. Bioucas-Dias,et al.  Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Caroline Fossati,et al.  Improvement Classification for Hyperspectral Image Based on Tensor Modelling , 2010 .

[10]  Caroline Fossati,et al.  Denoising of Hyperspectral Images Using the PARAFAC Model and Statistical Performance Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Karen O. Egiazarian,et al.  Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction , 2013, IEEE Transactions on Image Processing.

[12]  Levent Sendur,et al.  A bivariate shrinkage function for wavelet-based denoising , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  Chein-I Chang,et al.  Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Filiberto Pla,et al.  Effect of Denoising in Band Selection for Regression Tasks in Hyperspectral Datasets , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Liangpei Zhang,et al.  Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–Spatial Feature Extraction , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Salah Bourennane,et al.  Survey on tensor signal algebraic filtering , 2007, Signal Process..

[17]  Guangyi Chen,et al.  Denoising of Hyperspectral Imagery Using Principal Component Analysis and Wavelet Shrinkage , 2011, IEEE Transactions on Geoscience and Remote Sensing.