高永刚, 徐涵秋. 利用直方图匹配和低通滤波改进的SVR融合算法[J]. 武汉大学学报 ( 信息科学版), 2012, 37(11): 1316-1320.
引用本文: 高永刚, 徐涵秋. 利用直方图匹配和低通滤波改进的SVR融合算法[J]. 武汉大学学报 ( 信息科学版), 2012, 37(11): 1316-1320.
GAO Yonggang, XU Hanqiu. An Improved SVR Image Fusion Algorithm Base on Low-pass Filter and Histogram Matching[J]. Geomatics and Information Science of Wuhan University, 2012, 37(11): 1316-1320.
Citation: GAO Yonggang, XU Hanqiu. An Improved SVR Image Fusion Algorithm Base on Low-pass Filter and Histogram Matching[J]. Geomatics and Information Science of Wuhan University, 2012, 37(11): 1316-1320.

利用直方图匹配和低通滤波改进的SVR融合算法

An Improved SVR Image Fusion Algorithm Base on Low-pass Filter and Histogram Matching

  • 摘要: 针对SVR融合算法光谱失真的现象,提出了一种基于低通滤波和直方图匹配的改进SVR算法,即SVRFM算法。以IKONOS影像为实验数据,从光谱保真度和高频信息融入度两个方面将SVRFM与小波变换、SVR、Pansharp、Ehlers和Gram-Schmidt等算法的融合结果进行了定性和定量对比。结果表明,SVRFM算法的融合影像光谱信息失真小,高频信息融入度高。

     

    Abstract: To avoid the spectral distortion of SVR(synthetic variable ratio) algorithm,we propose an improved algorithm by using a low-pass filter and histogram matching performance,which is hence named SVR based on low-pass filter and histogram matching(SVRFM) algorithm.Two subsets from the IKONOS image of Fuzhou,representing different land cover types were used as test data.The spectral fidelity and the ability of gaining high frequency information were assessed by using visual and statistical analysis.The fused images were compared with those fused using the SVR,wavelet transform,pansharp,ehlers and Gram-Schmidt algorithms,respectively.The results show that the spectral fidelity of the SVRFM algorithm is generally better than the five algorithms compared.

     

/

返回文章
返回