An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform. (January 2018)

• Record Type: Journal Article
• Authors: Anandhi, D.; Valli, S.
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 139
• Page End: 152
• Publication Date: 2018-01
• Subjects: Despeckling -- Maximum a posteriori estimation -- Nonsubsampled Contourlet Transform -- Synthetic Aperture Radar -- Satellite image fusion; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.04.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Multi-sensor image fusion draws an inference based on the information obtained from different sensors. Recently, wavelet and contourlet transforms have been widely used in multi-sensor image fusion. But these transforms have been inadequate in the representation of images due to their subsampling. Hence, a fusion algorithm based on Synthetic Aperture Radar (SAR) and Panchromatic (PAN) images in Nonsubsampled Contourlet Transform (NSCT) domain is proposed. NSCT gives flexible multiscale, multidirectional expansion for images. A high fusion accuracy is achieved by 'Maximum A Posteriori (MAP)' estimation based on Rayleigh and Laplacian probabilities for despeckling of SAR higher frequency coefficients. Subsequently, the despeckled SAR coefficient is directly fused with PAN coefficients using the newly developed Edge-based fusion rule. The combination of NSCT, MAP and Edge-fusion rule facilitates maximum preservation of the edge and the texture information. The performance of the proposed fusion algorithm is evaluated using reference and non-reference quality metrics. The results prove that the proposed method outperforms the existing NSCT methods by preserving maximum features.