Morphology-based spatial filtering for efficiency enhancement of remote sensing image fusion. (January 2021)

• Record Type: Journal Article
• Authors: Pandit, Vaibhav R.; Bhiwani, R.J.
• Is Part Of: Computers & electrical engineering. Volume 89(2021)
• Publication Date: 2021-01
• Subjects: Remote sensing -- Image fusion algorithm -- Morphology operations -- Multi-spectral image -- Panchromatic image -- Deep learning; 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.2020.106945
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Better visual interpretation and high-level feature extraction are always desired for remotely sensed image processing applications. The fulfillment is most prominent when a Multi-Spectral (MS) image fused with a PANchromatic (PAN) image for the same geographic location produces another MS image with added spatial resolution. Development of such fusion algorithms based on approaches like Component Substitution (CS) and Multi-Resolution Analysis (MRA) is continuously researched over the last few decades. In this paper, the authors propose the morphological operator-based image fusion algorithm featuring nonlinear decomposition. Also, the use of morphological operator based spatial filtering is successfully demonstrated for efficiency enhancement of the proposed and a few of the sophisticated image fusion algorithms based on CS, MRA, and state-of-the-art deep learning approach. The potential of the presented work is proved through reduced- and full-resolution assessment procedures utilizing two data sets acquired by WorldView-3 and WorldView-2 satellite sensors. Graphical abstract: Highlights: Efficiency of image fusion is enhanced using morphology-based spatial filtering. The performance evaluation of twenty-eight image fusion algorithms is carried out. Data set images of WorldView-2 and WorldView-3 satellites are used.