基于信息熵的加权块稀疏子空间聚类算法

Weighted Block Sparse Subspace Clustering Algorithm Based on Information Entropy

稀疏子空间聚类(Sparse subspace clustering,SSC)算法在处理高光谱遥感影像时,地物的划分精度较低,为了提高地物划分精度,本文提出了一种基于信息熵的加权块稀疏子空间聚类算法(Weighted block sparse subspace clustering algorithm based on information entropy,EBSSC)。信息熵权重与块对角约束的引入,可以在仿真实验前获得两像素属于同一类别的先验概率,从而正向干预模型求解出的解趋于块对角结构的最优近似解,使模型获得对抗噪声和异常值的性能,从而提高模型分类的判别能力,以获得更好的地物划分精度。在3个经典高光谱遥感数据集上的实验结果表明,本文算法聚类高光谱影像的效果优于现有的几个经典流行的子空间聚类算法。

When the sparse subspace clustering algorithm processes hyperspectral remote sensing images,the classification accuracy of features is low.In order to improve the accuracy of feature classification,this paper proposes a weighted block sparse subspace clustering algorithm based on information entropy(EBSSC).The introduction of information entropy weight and block diagonal constraint can obtain the prior probability that two pixels belong to the same category before the simulation experiment,so that the solution solved by the positive intervention model becomes the optimal approximate solution of the block diagonal structure,making the model obtain the performance against noise and outliers,thereby improving the discriminative ability of model classification to obtain better classification accuracy of ground features.Experimental results on three classical hyperspectral remote sensing data sets show that the clustering effect of hyperspectral image in this paper is better than that of several existing classical and popular subspace clustering algorithms.