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

Authors

  • Yuan De Bao College of Geoscience and Surveying Engineering
  • Hong Xueqian
  • Yu Shiwei
  • Li Lianjian
  • Zha Yanbo

DOI:

https://doi.org/10.3991/ijoe.v10i3.3686

Keywords:

data fusion algorithm, PAN and multi-spectral, algorithm evaluation, ETM

Abstract


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.

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Published

2014-04-28

How to Cite

Bao, Y. D., Xueqian, H., Shiwei, Y., Lianjian, L., & Yanbo, Z. (2014). Analysis of Four Remote Image Fusion Algorithms for Landsat7 ETM+ PAN and Multi-spectral Imagery. International Journal of Online and Biomedical Engineering (iJOE), 10(3), pp. 49–52. https://doi.org/10.3991/ijoe.v10i3.3686

Issue

Vol. 10 No. 3 (2014)

Section

Papers

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