A novel hyperspectral anomaly detector based on low-rank representation and learned dictionary

@article{Niu2016ANH,
  title={A novel hyperspectral anomaly detector based on low-rank representation and learned dictionary},
  author={Yubin Niu and Bin Wang},
  journal={2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
  year={2016},
  pages={5860-5863}
}
The low-rank property of hyperspectral imagery is well exploited with low-rank decomposition methods recently. In our approach, a novel hyperspectral anomaly detector based on low-rank representation (LRR) and learned dictionary (LD) has been proposed. This method assumes that a two-dimensional matrix transformed from a three-dimensional hyperspectral imagery can be decomposed into two parts: a low rank matrix representing the background and a sparse matrix standing for the anomalies. Further… CONTINUE READING

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