文章基本信息

标题：Performance Analyzing of High Resolution Pan-sharpening Techniques: Increasing Image Quality for Classification using Supervised Kernel Support Vector Machine
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作者：Yuhendra ; Joshapat Tri Sumantyo ; Hiroaki Kuze 等
期刊名称：Research Journal of Information Technology
印刷版ISSN：1815-7432
电子版ISSN：2151-7959
出版年度：2011
卷号：3
期号：1
页码：12-23
DOI：10.3923/rjit.2011.12.23
出版社：Academic Journals Inc., USA
摘要：Pan-sharpening is also known as image fusion, resolution merge, image integration and multi sensor data fusion has been widely applied to imaging sensors. The purposes of pan-sharpening is to fuse a low spatial resolution multispectral image with a higher resolution panchromatic image to produces an image with higher spectral and spatial resolution. In this paper, we are investigated these existing pan-sharpening methods based on visual and spectral analysis. And to achieve assess the accurate classification process, we proposed a Support Vector Machine (SVM) based on Radial Basis Function (RBF) kernel. In the Experimental results, a comparative performance analysis of techniques by various methods show that Gram-Schmidt followed by PCA perform best among all the techniques. Besides that, higher overall accuracy of Gram-Smidth (GS) fused image increase 0.90 percent. And also, the high producers and users accuracy average of Gram-Smidth (GS) fused for each of the classes and methods used was always reported greater than 91.8% and 91.11%, respectively, indicating the overall success of the performed classification. And producer accuracy he followed by PCA was 90.84% and user accuracy was 89.99.