基于字典优化的联合稀疏表示高光谱图像分类

针对训练样本量少导致高光谱图像分类精度低的问题,本文提出了一种基于字典优化的联合稀疏表示高光谱图像分类方法.首先,采取基于层次聚类的波段选择方法降低高光谱图像数据维度;其次,结合空间信息将高光谱数据划分为多个子集,利用已知标签信息的训练样本标记各个子集中可能成为训练样本的像元,组成训练样本备选集,根据光谱相似度准则筛选...

Joint sparse representation of hyperspectral image classification based on dictionary optimization

Aiming at the problem of low training sample size leading to low classification accuracy of hyperspectral images, this paper proposes a joint sparse representation hyperspectral image classification method based on dictionary optimization. First, the band selection method based on hierarchical clustering is adopted to reduce the dimensionality of hyperspectral image data; second, the hyperspectral data is divided into multiple subsets based on spatial information, and training samples with known label information are used to mark each subset that may become training samples. Pixels form a candidate set of training samples, and the candidate set is filtered according to the spectral similarity criterion to obtain an optimized dictionary; finally, the optimized dictionary is used to classify hyperspectral images through joint sparse representation. The simulation experiments of Indian Pines dataset and Pavia University dataset show that the classification algorithm proposed in this paper can effectively improve the classification accuracy of hyperspectral images.