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Issue title: Soft computing and intelligent systems: Tools, techniques and applications
Guest editors: Sabu M. Thampi and El-Sayed M. El-Alfy
Article type: Research Article
Authors: Vishnu Pradeep, V.* | Sowmya, V. | Soman, K.P.
Affiliations: Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India
Correspondence: [*] Corresponding author. V. Vishnu Pradeep, Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore, India. Tel.: +919446644199; E-mail: [email protected].
Abstract: Remote sensing satellites are proficient in taking earth images across various regions in visible part of electromagnetic spectrum. The images can be panchromatic image of a single band, multispectral image of three to seven different bands, and hyperspectral image taken from about 220 contiguous spectral bands. These images are used together or on its own, depending on the significance and usage of the preferred application. Pan-sharpening is one method which is used to improve the quality of a low resolution multispectral image by fusion with a high resolution panchromatic image. This paper proposes a method based on M-band wavelets for the pan-sharpening of a low resolution multispectral image. The method tries to improve the spatial characteristics while preserving the spectral quality of the data. The proposed technique uses weighted fusion rule and average fusion rule. The data used for the experiment were acquired by high resolution optical imagers onboard QuickBird, WorldView-3, WorldView-2 and GeoEye-1. A comparison with existing fusion techniques is done based on image quality metrics and visual interpretation. The experimental results and analysis suggests that the proposed pan-sharpening technique outperforms other compared pre-existing pan-sharpening methods.
Keywords: M-band wavelets, pan sharpening, average fusion rule, weighted fusion rule, image quality metrics
DOI: 10.3233/JIFS-169258
Journal: Journal of Intelligent & Fuzzy Systems, vol. 32, no. 4, pp. 3151-3158, 2017
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