Abstract
AbstractIn this paper, we conduct a study for the effects of principal component analysis (PCA) on hyperspectral imagery denoising. Our previous works combined PCA with wavelet shrinkage, block matching and 3D filtering (BM3D), and block matching and 4D filtering (BM4D), respectively, and very good denoising results have been obtained for hyperspectral imagery with very little noise. To demonstrate if these methods are the best for other noise scenarios, we combine PCA with video BM3D (VBM3D) and video BM4D (VBM4D), and non-local means (NL-Means) as well. Experimental results show that the PCA+VBM3D is the best denoising method for moderate and high noise levels, and PCA+BM4D is preferred for very low noise levels. KeywordsHyperspectral imagery denoisingBM3DBM4DVBM3DVBM4DPrincipal component analysis (PCA)
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