Improved Adaptive Brovey as a New Method for Image Fusion
This work addresses image fusion for remote sensing applications, but it is incremental as it combines existing techniques.
The paper tackled the problem of color distortion in image fusion by integrating Brovey and contourlet techniques, resulting in improved merging quality as measured by correlation coefficient, ERGAS, UIQI, and Q4 compared to existing methods like IHS and PCA.
An ideal fusion method preserves the Spectral information in fused image and adds spatial information to it with no spectral distortion. Among the existing fusion algorithms, the contourlet-based fusion method is the most frequently discussed one in recent publications, because the contourlet has the ability to capture and link the point of discontinuities to form a linear structure. The Brovey is a popular pan-sharpening method owing to its efficiency and high spatial resolution. This method can be explained by mathematical model of optical remote sensing sensors. This study presents a new fusion approach that integrates the advantages of both the Brovey and the cotourlet techniques to reduce the color distortion of fusion results. Visual and statistical analyzes show that the proposed algorithm clearly improves the merging quality in terms of: correlation coefficient, ERGAS, UIQI, and Q4; compared to fusion methods including IHS, PCA, Adaptive IHS, and Improved Adaptive PCA.