Abstract
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.
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Foundation item: Under the auspices of the National Natural Science Foundation of China (No. 40171015).
Biography: ZHAO Shu-he (1971 -), male, a native of Shandong, Ph. D. His main research interests include information extraction of high spatial resolution remote sensing data, data fusion of multi-source remote sensing.
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Zhao, Sh., Xue-zhi, F., Kang, Gd. et al. Multi-source remote sensing image fusion based on support vector machine. Chin. Geograph.Sc. 12, 244–248 (2002). https://doi.org/10.1007/s11769-002-0009-9
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DOI: https://doi.org/10.1007/s11769-002-0009-9