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Multi-sensor data fusion using NIHS transform and decomposition algorithms

Publication Type : Journal Article

Publisher : Multimedia Tools and Applications

Source : Multimedia Tools and Applications, Volume 77, Number 23, p.30381–30402 (IF: 2.313, CiteScore: 3.7, Q1- 80 percentile) (2018)

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Keywords : Dynamic mode decomposition, Image empirical mode decomposition, Multi-spectral fusion, Non-linearIHS transform, Remote sensing

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2018

Abstract : Multi-spectral image fusion is to enhance the details present in multi-spectral bands with the spatial information available in the panchromatic image. Fused images have the effect of spectral distortions and lack of structural similarity. To overcome these limitations, three methods are proposed using intensity, hue, saturation (IHS) and nonlinear IHS (NIHS) transform along with the Dynamic Mode Decomposition (DMD) and 2D-Empirical Mode Decomposition (2D-EMD or IEMD). An intensity plane is calculated from the NIHS transform. The modes are constructed using DMD by considering the variations between the intensity plane computed using NIHS transforms of a low resolution multi-spectral image and a panchromatic image. Similarly, 2D-EMD is also used for image fusion. Modes are subjected to weighted fusion rule to get an intensity plane with spatial and edge information. Finally, the calculated intensity plane is concatenated along with the hue and saturation plane of low-resolution multi-spectral image and transformed into RGB color space. Thus, the fused images have high spatial and edge information on spectral bands. The experiments and its quality assessment assure that proposed methods perform better than the existing methods.

Cite this Research Publication : V. Ankarao, Sowmya V., and Dr. Soman K. P., “Multi-sensor Data Fusion using NIHS Transform and Decomposition Algorithms”, Multimedia Tools and Applications, vol. 77, pp. 30381–30402 (IF: 2.313, CiteScore: 3.7, Q1- 80 percentile), 2018.

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