基于最大最小距离的高光谱遥感图像波段选择

为减少高光谱遥感图像光谱空间冗余，降低后续处理的计算复杂度，提出一种基于最大最小距离的高光谱图像波段选择算法。首先计算波段标准差，选定标准差最大的波段作为初始中心；然后使用最大最小距离算法得到相对距离较远的聚类中心，对波段进行聚类；最后使用K中心点算法更新聚类中心。实验仿真结果表明:通过基于最大最小距离算法选择的波段，能够选出同时满足信息量大、相关性小的要求的波段子集，并将获得的波段组合用于高光谱图像分类时，可以得到较好的分类精度。

Maximin distance algorithm-based band selection for hyperspectral imagery

In this paper, we propose a hyperspectral-image band-selection algorithm based on the maximin distance to reduce the spectral redundancy of hyperspectral remote sensing images as well as the associated computational complexity. First, the algorithm computes the standard deviation of all bands and selects the one with the maximum standard deviation as the initial center. Then, to cluster the bands, we use the maximin distance algorithm to obtain centers that are relatively farther away. Finally, we use the k-medoids algorithm to update the clustering center. The experimental results show that the bands selected by the maximin distance algorithm can satisfy the demands associated with a large amount of information and relatively low correlation. At the same time, when the obtained bands are combined for hyperspectral image classification, higher classification accuracy can be achieved.