基于分层稀疏表示特征学习的高光谱图像分类研究

Research of Hyperspectral Image Classification Based on Hierarchical Sparse Representation Feature Learning

提出一种基于分层稀疏表示特征学习的方法即分层判别特征学习算法对高光谱图像进行分类,在两层的分层结构中用空间金字塔匹配模型在每层的稀疏编码上用最大池化方法学习得到判别特征,用分层判别特征学习得到的特征表示对于分类更稳健、判别性更好。在两个高光谱数据集上评价该方法,结果表明,该方法具有更好的分类精度。

A method of classification based on hierarchical sparse representation feature learning as hierarchical discriminative feature learning algorithm is developed for hyperspectral image classification. The spatial-pyramid- matching model is used, and the sparse codes learned from the discriminative features are obtained by max pooling in eaeh layer of the two-layer hierarchical structure. The representation of features achieved by the proposed method are more robust and discriminative for the classification. The proposed method is evaluated on two hyperspeetral datasets, and the results show that the proposed method has good classification accuracy.