Sparse representation classification on hyperspectral imagery based-multiple measurement vectors

作者：Sun Weiwei; Liu Chun; Li Weiyue

来源：Journal of Tongji University(Natural Science), 2016, 44(3): 454-461.

DOI：10.11908/j.issn.0253-374x.2016.03.018

• Classification accuracy
• Coefficient vector
• Computational time
• Hyper-spectral imageries
• Hyperspectral imagery
• Multiple measurement vectors
• Reconstruction error
• Sparse representation

Traditional sparse representation based classifiers ignore inter-connections among pixels and have high computational complexity when applied in hyperspectral imagery (HSI) field. Therefore, a multiple measurement vectors based sparse representation classifier (MMV-SRC) model is proposed to solve the above problems. The model introduces a balance parameter to control the sparsity of coefficient vectors, and estimates sparse coefficient vectors of all testing pixels by minimizing reconstruction errors using the L2-norm constraint. Experiments on two HSI datasets are implemented to test the performance of MMV-SRC, and the results are compared with those of five state-of-the-art classifiers. The results show that MMV-SRC achieves best classification accuracies among all whereas taking the second shortest computational time.

• 出版日期2016
• 单位同济大学