激光与光电子学进展, 2016, 53 (9): 091001, 网络出版: 2016-09-14
基于分层稀疏表示特征学习的高光谱图像分类研究 下载: 651次
Research of Hyperspectral Image Classification Based on Hierarchical Sparse Representation Feature Learning
遥感 高光谱图像分类 特征学习 稀疏表示 remote sensing hyperspectral image classification feature learning sparse representation
摘要
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。
Abstract
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 each 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 hyperspectral datasets, and the results show that the proposed method has good classification accuracy.
李铁, 孙劲光, 张新君, 王星. 基于分层稀疏表示特征学习的高光谱图像分类研究[J]. 激光与光电子学进展, 2016, 53(9): 091001. Li Tie, Sun Jinguang, Zhang Xinjun, Wang Xing. Research of Hyperspectral Image Classification Based on Hierarchical Sparse Representation Feature Learning[J]. Laser & Optoelectronics Progress, 2016, 53(9): 091001.