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基于模糊贴近度和改进Prim算法的高光谱图像波段分组排序

DOI: 10.6046/gtzyyg.2014.04.02, PP. 8-13

Keywords: 高光谱图像,波段分组排序,Prim算法,模糊贴近度,最大最小贴近度(MMC)

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Abstract:

传统的基于Prim算法的高光谱图像波段分组排序需要计算所有波段之间的相关系数,并采用满秩相关系数矩阵作为邻接矩阵进行比较,计算复杂度较高。结合模糊数学的相似度衡量理论和高光谱图像的特点,首先提出使用计算简单的最大最小贴近度(maximumandminimumcloseness,MMC)作为衡量高光谱图像波段间相关性的参数;然后将MMC的满秩邻接矩阵稀疏化,提取有效波段进行排序,明显降低了排序的波段数目和比较次数。实验结果表明,与传统的Prim算法相比,所提出的算法在保持原有压缩效率的同时,大大降低了波段排序的复杂度,平均波段排序运行时间减少了27%。

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