基于核模糊粗糙集的高光谱波段选择算法

为了减少高光谱波段图像间的冗余，降低运算时间，为后续分类任务提供有效支持，提出了基于核模糊粗糙集的高光谱波段选择算法。高光谱图像相邻波段间相似性较强，为进一步有效地度量波段的重要性，引入核模糊粗糙集理论。考虑波段中类的分布特性，根据波段的下近似集分布定义波段间的相关性，进而结合波段的信息熵定义波段的重要度。采用最大相关性最大重要度的搜索策略对高光谱图像进行波段选择。最后在常用高光谱数据集Indiana Pines农业区上，采用J48及KNN分类器进行测试。与其他高光谱波段选择算法相比，该算法在两个分类器上的总体平均分类精度分别提升了4.5和6.6个百分点。实验结果表明所提算法在处理高光谱波段选择问题时具有一定优势。

Hyperspectral band selection algorithm based on kernelized fuzzy rough set

In order to reduce the redundancy between hyperspectral band images, decrease the computing time and facilitate the following classification task, a hyperspectral band selection algorithm based on kernelized fuzzy rough set was proposed. Due to strong similarity between adjacent bands of hyperspectral images, the kernelized fuzzy rough set theory was introduced to measure the importance of bands more effectively. Considering the distribution characteristics of categories in the bands, the correlation between bands was defined according to the distribution of the lower approximate set of bands, and then the importance of bands was defined by combining the information entropy of bands. The search strategy of maximum correlation and maximum importance was used to realize the band selection of hyperspectral images. Finally, experiments were conducted on the commonly used hyperspectral dataset Indiana Pines agricultural area by using the J48 and KNN classifiers. Compared with other hyperspectral band selection algorithms, this algorithm has overall average classification accuracy increased by 4.5 and 6.6 percentage points respectively with two classifiers. The experimental results show that the proposed algorithm has some advantages in hyperspectral band selection.