Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping

被引:212
作者
Chang, Yi [1 ]
Yan, Luxin [1 ]
Fang, Houzhang [2 ]
Luo, Chunan [1 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Automat, Sci & Technol Multispectral Informat Proc Lab, Wuhan 430074, Peoples R China
[2] Xidian Univ, Natl Lab Radar Signal Proc, Xian 710071, Peoples R China
基金
中国国家自然科学基金;
关键词
Destriping; denoising; spectral-spatial total variation; split Bregman iteration; remote sensing image; UNIDIRECTIONAL TOTAL VARIATION; SENSED IMAGES; MODIS DATA; RESTORATION; REMOVAL; DECOMPOSITION; ALGORITHMS; WAVELET; STRIPE;
D O I
10.1109/TIP.2015.2404782
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multispectral remote sensing images often suffer from the common problem of stripe noise, which greatly degrades the imaging quality and limits the precision of the subsequent processing. The conventional destriping approaches usually remove stripe noise band by band, and show their limitations on different types of stripe noise. In this paper, we tentatively categorize the stripes in remote sensing images in a more comprehensive manner. We propose to treat the multispectral images as a spectral-spatial volume and pose an anisotropic spectral-spatial total variation regularization to enhance the smoothness of solution along both the spectral and spatial dimension. As a result, a more comprehensive stripes and random noise are perfectly removed, while the edges and detail information are well preserved. In addition, the split Bregman iteration method is employed to solve the resulting minimization problem, which highly reduces the computational load. We extensively validate our method under various stripe categories and show comparison with other approaches with respect to result quality, running time, and quantitative assessments.
引用
收藏
页码:1852 / 1866
页数:15
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