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两种高保真遥感影像融合方法比较
引用本文:李存军,刘良云,王纪华,王人潮.两种高保真遥感影像融合方法比较[J].中国图象图形学报,2004,9(11):1376-1385.
作者姓名:李存军  刘良云  王纪华  王人潮
作者单位:[1]浙江大学农业遥感与信息技术应用研究所,杭州310029//国家农业信息化工程技术研究中心,北京100089 [2]国家农业信息化工程技术研究中心,北京100089 [3]浙江大学农业遥感与信息技术应用研究所,杭州310029
基金项目:国家“8 63”研究计划项目 (2 0 0 3 AA2 0 90 40 )
摘    要:遥感影像融合有着广泛的应用前景。定量遥感不仅要求影像融合提高空间分辨率,更重要的是保持影像光谱信息,减少失真。为了使人们对不同遥感影像融合方法优缺点有一概略了解,首先详细介绍了两种新的高保真融合算法(基于亮度调节的平滑滤波和Gram-Schmidt变换)的原理和方法;然后以城区IKONOS影像为数据源,通过目视判别、定量统计参数和图形法3种方法对两种融合算法进行了比较,并与传统的融合算法IHS变换和PC变换进行了对比。结果表明,4种融合算法的空间效果是相似的,但从对光谱信息的保真来看,PC变换和IHS变换都较差,基于亮度调节的平滑滤波保真效果最好,Gram-Schmidt变换次之,但Gram-Schmidt变换保真效果已比PC变换和IHS变换有了较大的提高。

关 键 词:融合算法  遥感影像  PC  IHS变换  融合方法  定量遥感  数据源  高保真  亮度调节  平滑滤波
文章编号:1006-8961(2004)11-1376-10

Comparison of Two Methods of Fusing Remote Sensing Images with Fidelity of Spectral Information
LI Cun-jun ,LIU Liang-yun ,WANG Ji-hua ,WANG Ren-chao ,LI Cun-jun ,LIU Liang-yun ,WANG Ji-hua ,WANG Ren-chao ,LI Cun-jun ,LIU Liang-yun ,WANG Ji-hua ,WANG Ren-chao and LI Cun-jun ,LIU Liang-yun ,WANG Ji-hua ,WANG Ren-chao.Comparison of Two Methods of Fusing Remote Sensing Images with Fidelity of Spectral Information[J].Journal of Image and Graphics,2004,9(11):1376-1385.
Authors:LI Cun-jun  LIU Liang-yun  WANG Ji-hua  WANG Ren-chao  LI Cun-jun  LIU Liang-yun  WANG Ji-hua  WANG Ren-chao  LI Cun-jun  LIU Liang-yun  WANG Ji-hua  WANG Ren-chao and LI Cun-jun  LIU Liang-yun  WANG Ji-hua  WANG Ren-chao
Abstract:There is a great application potential for the fusion of remote sensing images. With the development of quantitative remote sensing, not only improving spatial details but also preserving the spectral information of multispectral bands were required. The principle and methods of two fusion algorithms,SFIM (smoothing filter-based intensity modulation) and Gram-Schmidt (Gram-Schmidt transform), were described. In a case of IKONOS image in city, visual judgment, quantitative statistical parameters and graphs comparison were used to assess these two algorithms, which were also compared to the traditional methods of IHS transform and PC(principal component) transform. The results showed there was no distinct difference in spatial details improved. However in terms of spectral information fidelity, both IHS and PC method were the worst, Gram-Schmidt method was better, while SFIM method was the best.
Keywords:remote sensing  fusion  fidelity  smoothing filter-based intensity modulation(SFIM)  Gram-Schmidt  comparison
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