Acta Optica Sinica, Volume. 32, Issue 3, 330003(2012)
Hyper-Spectrum Classification Based on Sparse Representation Model and Auto-Regressive Model
A novel classification approach based on sparse representation model and auto-regressive model is presented to deal with spectral and spatial information underutilization effectively for hyper-spectrum classification. The combination dictionary is designed using sparse representation model and auto-regressive model. Sparse representation model is used to represent every spectral vector as sparse linear combination of the training samples on spectral dimension; auto-regressive model is added to constrain every spectral vector by its eight neighborhoods on spatial dimension. A new dictionary is constructed for every class to reduce the computation and reconstruction error. At last, the sparse problem is recovered by solving a constrained optimization of minimum reconstruction error and neighboring relativity. The classification of hyper-spectral image is determined by computing the minimum reconstruction error of testing samples and training samples. Simulation results show that the method improves the classification accuracy.
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Song Lin, Cheng Yongmei, Zhao Yongqiang. Hyper-Spectrum Classification Based on Sparse Representation Model and Auto-Regressive Model[J]. Acta Optica Sinica, 2012, 32(3): 330003
Category: Spectroscopy
Received: Sep. 16, 2011
Accepted: --
Published Online: Feb. 15, 2012
The Author Email: Lin Song (linsong0818@gmail.com)