HS remote sensing image restoration using fusion with MS images by EM algorithm

被引:11
作者
Ansari, Amir [1 ]
Danyali, Habibollah [1 ]
Helfroush, Mohammad Sadegh [1 ]
机构
[1] Shiraz Univ Technol, Dept Elect & Elect Engn, Shiraz, Iran
关键词
remote sensing; image restoration; image fusion; hyperspectral imaging; expectation-maximisation algorithm; image denoising; wavelet transforms; Gaussian processes; mixture models; geophysical image processing; HS remote sensing image restoration; MS images; EM algorithm; mineral exploration; agricultural application; hyperspectral image quality; degraded HS image restoration; multispectral observation; HS image-MS image fusion; maximum a posteriori estimation; expectation maximisation algorithm; deblurring; denoising; spatial domain; nonoverlapping blocks; wavelet domain; multinormal model; Gaussian scale mixture; airborne visible-infrared imaging spectrometer; HS digital imagery collection experiment; HYDICE database; signal-to-noise ratio; Moffett database; BAYESIAN FUSION; WAVELET; SUPERRESOLUTION;
D O I
10.1049/iet-spr.2016.0141
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Remote sensing images are widely used for different areas from mineral exploration to agricultural applications and poor quality of hyperspectral (HS) images will directly have adverse effect on these applications. In this study, a method is proposed to restore degraded HS images. To achieve this aim, another multispectral (MS) observation of the same scene is supposed to be available and restoration is fulfilled by fusion of HS images and MS images. The proposed method gains maximum a posteriori estimation and is based on expectation maximisation algorithm. Deblurring and denoising are performed separately. Deblurring is done in spatial domain via non-overlapping blocks, whereas denoising is implemented in wavelet domain. To represent the coefficients in wavelet domain, instead of multinormal model, Gaussian scale mixture is exploited. The proposed method is validated on airborne visible/infrared imaging spectrometer (AVIRIS) and HS digital imagery collection experiment (HYDICE) databases and experimental results signify that the proposed method outperforms state-of-the-art techniques cited in the literature and signal-to-noise ratio is improved as much as 15.71dB for Moffett database and 16.26dB for HYDICE database.
引用
收藏
页码:95 / 103
页数:9
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