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

Shu he Zhao*, Xue zhi Feng, Guo ding Kang, Elnazir Ramadan

*المؤلف المقابل لهذا العمل

نتاج البحث: المساهمة في مجلةمراجعة النظراء

6 اقتباسات (Scopus)

ملخص

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.

اللغة الأصليةEnglish
الصفحات (من إلى)244-248
عدد الصفحات5
دوريةChinese Geographical Science
مستوى الصوت12
رقم الإصدار3
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2002
منشور خارجيًانعم

ASJC Scopus subject areas

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