@inproceedings{7751dc1a786b48e2b413e758dae6dc4c,
title = "Hyperspectral Image Classification VIA a Joint Sparsity and Spatial Correlation Model",
abstract = "In this paper, a novel constrained Sparse Representation (SR) algorithm based on the joint sparsity and spatial correlation for hyper-spectral image (HSI) classification is proposed. The coefficients in the sparse vector associated with the training samples in the structured dictionary exhibit the group sparsity continuity. However, this joint sparsity of the coefficient vector is not considered in the classical SR classifiers. In addition, spatial correlation has positive effect on HSI classification processing. Thus in the proposed SR model, we consider a joint sparsity regularization term to promote the joint sparsity of the sparse vectors and use space regularization to restrict spatial correlation of the output. The formulated problem is solved via the alternating direction method of multipliers (ADMM). Simulation results show that the proposed algorithm has the improved performance.",
keywords = "ADMM, classification, Hyperspectral imagery, joint sparsity, sparse representation",
author = "Xiaoli Yang and Zeng Li and Jie Chen and Yi Zhang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th International Conference on Wireless Communications and Signal Processing, WCSP 2018 ; Conference date: 18-10-2018 Through 20-10-2018",
year = "2018",
month = nov,
day = "30",
doi = "10.1109/WCSP.2018.8555626",
language = "英语",
series = "2018 10th International Conference on Wireless Communications and Signal Processing, WCSP 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 10th International Conference on Wireless Communications and Signal Processing, WCSP 2018",
}