a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise- smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by l 2,1 -norm regularization.
Please Cite the Paper "Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery" as below:
L. Li, W. Li, Y. Qu, C. Zhao, R. Tao and Q. Du, "Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2020.3038659.
Or Latex format
@ARTICLE{9288702, author={L. {Li} and W. {Li} and Y. {Qu} and C. {Zhao} and R. {Tao} and Q. {Du}}, journal={IEEE Transactions on Neural Networks and Learning Systems}, title={Prior-Based Tensor Approximation for Anomaly Detection in Hyperspectral Imagery}, year={2020}, volume={}, number={}, pages={1-14}, doi={10.1109/TNNLS.2020.3038659}}
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