Analysis of Four Remote Image Fusion Algorithms for Landsat7 ETM+ PAN and Multi-spectral Imagery

Yuan De Bao, Hong Xueqian, Yu Shiwei, Li Lianjian, Zha Yanbo


This study takes the southeastern part of Beijing as an example to compare four remote image fusion algorithms for improving the visualization of Landsat7 ETM+ imagery. This paper introduces four remote image fusion algorithms including the Smoothing Filter Based Intensity Modulation (SFIM), High Pass Filter (HPF) Transform, Brovey Transform, and Multiplication (MLT) Transform. The effectiveness of the four remote image fusion algorithms is evaluated based on different quantitative indexes, including mean, deviation, information entropy, average gradient and correlation. The study reveals that the SFIM transform is the best method to remain spectral information of the original remote image, which does not cause spectral distortion and has highest spatial frequency information. Moreover, the fused remote images from the same sensor system are of high quality and can be used for improving the latter visual interpretation.


data fusion algorithm; PAN and multi-spectral; algorithm evaluation; ETM+

Full Text:


International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
Creative Commons License
Scopus logo Clarivate Analyatics ESCI logo IET Inspec logo DOAJ logo DBLP logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo