基于模糊c均值算法和人工蜂群算法的无监督波段选择
Unsupervised Band Selection Based on Fuzzy c-Means Algorithm and Artificial Bee Colony Algorithm
波段选择是高光谱影像处理中一种重要的降维方法. 在类标签不可获得的情况下, 如何选择出一个具有代表性的波段子集是一个挑战性的问题. 为了解决高光谱数据维数灾难以及光谱空间冗余的问题, 基于模糊c均值算法(Fuzzy c-means, FCM),人工蜂群算法(Artificial Bee Colony, ABC) 与极 大熵准则(Maximum Entropy, ME),文章提出了一种新的无监督波段选择方法. 该方法首先通过FCM算法将相似的波段划分到一个波段子集中,然后以ME为ABC算法中的适应度函数, 寻 找优化的波段子集.为验证该算法的有效性, 在三个典型的高光谱数据集上, 将所提出的方法和其它一些有效的波段选择算法进行了分类精度和计算时间对比. 实验结果表明,所提出的算法不但可以得到高的分类精度, 同时在计算时间上也具有明显的优势.
Band selection is an important dimension reduction technique in hyperspectral image processing. Under the condition that class labels are unavailable, how to select a representative band subset becomes a severe challenge. To address the problem of dimension disaster and spectral space redundancy, a novel unsupervised band selection method is introduced based on fuzzy c-means algorithm (FCM), artificial bee colony algorithm (ABC) and maximum Entropy (ME). In this method, the similar bands are firstly divided into a band subset by FCM algorithm, and then ME is adopted as the fitness function in the ABC algorithm to find the optimized band subset. To verify the effectiveness of the proposal, the proposed method is compared with other efficient band selection algorithms on three typical hyperspectral datasets. Experimental results show that the proposed algorithm not only can achieve high classification accuracy, but also has obvious advantages in computing time.
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