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（maximum and minimum closeness，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%。
Traditional hyperspectral image band grouping and reordering algorithms based on Prim require calculating the correlation coefficients between all bands, and full rank correlation coefficient matrix is used as the adjacent matrix for comparison, which causes high computational complexity. Combining the similarity measurement of fuzzy mathematics theory with the characteristics of the hyperspectral image, the maximum and minimum closeness(MMC)which possesses the characteristics of less computation is used as a parameter for measuring the correlation of the hyperspectral image bands. Then the adjacent matrix of MMC is processed into a sparse matrix and the effective bands is extracted for reordering. In this way, the number of bands used for ordering and the required times for band comparison will be significantly reduced. Experimental results show that, compared with the traditional Prim algorithm, the proposed algorithm greatly reduces the calculation complexity of the hyperspectral image band ordering while maintaining compression efficiency, and the average running time for band ordering has been reduced by 27%.
张转, 马玉, 蔡伟. 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[J]. 国土资源遥感, 2014, 26(4): 8-13.
ZHANG Zhuan, MA Yu, CAI Wei. Hyperspectral image band grouping and reordering based on fuzzy similarity and improved Prim algorithm. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 8-13.
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