Title :
A noise-adjusted iterative randomized singular value decomposition method for hyperspectral image denoising
Author :
Wei He ; Hongyan Zhang ; Liangpei Zhang ; Huanfeng Shen
Author_Institution :
State Key Lab. of Inf. Eng. in Surveying, Wuhan Univ., Wuhan, China
Abstract :
In this paper, a new denoising algorithm is proposed for hyperspectral image data cubes. With the strong correlations of the image bands, the low-rank structure of the hyperspectral image is explored by lexicographically ordering the 3-D data cube into 2-D matrix. Based on this property, the traditional principal component analysis (PCA) denoising model is established. For hyperspectral images (HSIs), the noise intensity in different bands is different. Therefore, a noise-adjusted iterative randomized singular value decomposition (NAIRSVD) algorithm is proposed to solve this PCA model. Combined with adaptive noise estimation and upper bound rank estimation, the proposed NAIRSVD algorithm is free from manual parameter determination. Several experiments were conducted to illustrate the performance of the proposed algorithm.
Keywords :
adaptive estimation; correlation methods; geophysical image processing; hyperspectral imaging; image denoising; iterative methods; principal component analysis; random processes; singular value decomposition; 2D matrix; HSI; NAIRSVD algorithm; PCA model; adaptive noise estimation; hyperspectral image data cube; hyperspectral image denoising; image band correlation; lexicographically 3D data cube ordering; low-rank structure; noise-adjusted iterative randomized singular value decomposition algorithm; principal component analysis; upper bound rank estimation; Approximation algorithms; Hyperspectral imaging; Noise; Noise reduction; Principal component analysis; NAIRSVD; PCA; denoising; hyperspectral image; low rank;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International
Conference_Location :
Quebec City, QC
DOI :
10.1109/IGARSS.2014.6946731
