Multi-source remote sensing image fusion based on support vector machine

Shu H. Zhao, Xue Z. Feng, Guo ding Kang, Elnazir Ramadan

Research output: Contribution to journalArticle

4 Citations (Scopus)


Remote Sensing image fusion is an effective way to use the large volume of data from multi-source images. This paper introduces a new method of remote sensing image fusion based on support vector machine (SVM), using high spatial resolution data SPIN-2 and multi-spectral remote sensing data SPOT-4. Firstly, the new method is established by building a model of remote sensing image fusion based on SVM. Then by using SPIN-2 data and SPOT-4 data, image classification fusion is tested. Finally, an evaluation of the fusion result is made in two ways. 1) From subjectivity assessment, the spatial resolution of the fused image is improved compared to the SPOT-4. And it is clearly that the texture of the fused image is distinctive. 2) From quantitative analysis, the effect of classification fusion is better. As a whole, the result shows that the accuracy of image fusion based on SVM is high and the SVM algorithm can be recommended for application in remote sensing image fusion processes.

Original languageEnglish
Pages (from-to)244-248
Number of pages5
JournalChinese Geographical Science
Issue number3
Publication statusPublished - 2002



  • Image fusion
  • Multi-spectral image
  • Panchromatic image
  • SVM

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Geography, Planning and Development

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