基于PCA与协同表示的高光谱图像分类研究

In traditional collaborative representation (CR) based hyperspectral image classification, the training samples are directly used to construct a dictionary for representation. However, the correlation among the training samples within a class tends to degrade the performance of collaborative representation based classification. In the paper, the principal component analysis (PCA) is used to de-correlate the training samples within a class. As a result, the influence of correlation among training samples on subsequent collaborative representation-based classification can be alleviated. Experimental results on two benchmark datasets show that the proposed algorithm can effectively improve the performance of traditional collaborative representation-based classification.