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基于MAP的高光谱图像超分辨率方法
引用本文:王立国,赵妍.基于MAP的高光谱图像超分辨率方法[J].光谱学与光谱分析,2010,30(4).
作者姓名:王立国  赵妍
作者单位:哈尔滨工程大学信息与通信工程学院,黑龙江,哈尔滨,150001
基金项目:国家自然科学基金项目,教育部博士点新教师基金项目,水下智能机器人技术国防科技重点实验室项目资助 
摘    要:高光谱图像得到了越来越广泛的应用,但较低的空间分辨率严重地影响着它的应用效果;其超分辨率方法受到学术界的高度重视,但一直没有得到很好的解决。为此重点研究了建立低分辨率资源图像与高分辨率目标图像之间的关系模型;引入关联感兴趣光谱端元的算子进行空间变换;应用最大后验概率(MAP)算法实现超分辨率复原。实验表明,该超分辨率方法具有超分辨率效果好、复杂度低、抗噪声性能强和保护感兴趣类别等优点。

关 键 词:高光谱图像  超分辨率  最大后验概率(MAP)  光谱端元(端元)

MAP Based Super-Resolution Method for Hyperspectral Imagery
WANG Li-guo,ZHAO Yan.MAP Based Super-Resolution Method for Hyperspectral Imagery[J].Spectroscopy and Spectral Analysis,2010,30(4).
Authors:WANG Li-guo  ZHAO Yan
Abstract:Hyperspectral imagery (HSI) is used in more and more fields, but its low spatial resolution limits its applications severely. The super-resolution algorithm catches more and more eyes but has not been solved well. In this case, the present paper aimed to do the following researches. The relation modeling was constructed between observed HSI of low resolution and target HSI of high resolution. In the modeling, space transformation was implemented by introducing the operator related to endmembers (EMs) of interest. Maximum posterior probability (MAP) algorithm was used to realize the super-resolution (SR) recovery. Experiments show that the proposed SR method has good recovery effect, low computational complexity, robust noise resistance, and can preserve classes of interest.
Keywords:Hyperspectral imagery(HSI)  Super-resolution  Maximum a posterior probability (MAP)  Endmember
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