基于PCA与协同表示的高光谱图像分类研究

Hyperspectral Image Classification Algorithm Based on PCA and Collaborative Representation

在基于协同表示(CR)的高光谱图像分类问题中,通常直接选用训练样本构建各类字典,但各类字典内训练样本基元间的相关性往往会降低协同表示分类性能。为此,该文提出采用主成分分析(PCA)对各类训练样本进行去相关处理以构建字典,降低了训练样本间的相关性对分类结果的影响,可有效提高协同表示分类的有效性。高光谱真实数据分类实验结果表明,该算法可有效提高传统协同表示分类的正确率。

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.