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

© The Institution of Engineering and Technology
Received 14/03/2016, Accepted 28/06/2016, Revised 04/06/2016, Published 18/08/2016

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 15.71 dB for Moffett database and 16.26 dB for HYDICE database.

Inspec keywords: remote sensing; wavelet transforms; Gaussian processes; geophysical image processing; mixture models; image denoising; hyperspectral imaging; image restoration; image fusion; expectation-maximisation algorithm

Other keywords: spatial domain; wavelet domain; EM algorithm; deblurring; HS image-MS image fusion; mineral exploration; multinormal model; HS remote sensing image restoration; Moffett database; airborne visible-infrared imaging spectrometer; degraded HS image restoration; signal-to-noise ratio; MS images; expectation maximisation algorithm; nonoverlapping blocks; hyperspectral image quality; Gaussian scale mixture; maximum a posteriori estimation; HS digital imagery collection experiment; multispectral observation; HYDICE database; denoising; agricultural application

Subjects: Computer vision and image processing techniques; Integral transforms; Function theory, analysis; Other topics in statistics; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Geophysical techniques and equipment; Probability theory, stochastic processes, and statistics; Integral transforms; Geophysics computing; Other topics in statistics; Optical, image and video signal processing; Data and information; acquisition, processing, storage and dissemination in geophysics

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