为了有效改善高光谱图像数据分类的精确度,减少对大数目数据集的依赖,在原型空间特征提取 方法的基础上,提出一种基于加权模糊C均值算法方案。该方案通过加权模糊 C均值算法对每个特征施加不同的权重,从而保证提取后的特征含有较高的信息量。实验结果表明,与业 内公认的原型空间提取算法相比,该方案在相对较小的数据集下,具有较为理想的稳定性,较高的分类精度, 大大降低了对数据集样本数量的依赖性,同时改善了原型空间特征方法的效率。

In order to improve the classification accuracy of hyperspectral image data, reduce dependence on large number of data sets, an improved method was proposed for feature extraction of hyperspectral data based on the weighted fuzzy C means algorithm. The approach is an extension of previous approach-prototype space feature extraction. Each feature with different weights in terms of weighted fuzzy C means algorithm to ensure the features contain more information after extracted. Experiment results show that compared to results obtained from approach prototype spatial feature extraction method, this method has a stability of data set and higher classification accuracy when extracted a small number of features, which greatly reduces the dependence on the number of data sets of samples, and improves the efficiency of the prototype spatial characteristics method.