Patch Matching-Based Multitemporal Group Sparse Representation for the Missing Information Reconstruction of Remote-Sensing Images

@article{Li2016PatchMM,
  title={Patch Matching-Based Multitemporal Group Sparse Representation for the Missing Information Reconstruction of Remote-Sensing Images},
  author={Xinghua Li and Huanfeng Shen and Huifang Li and Liangpei Zhang},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2016},
  volume={9},
  pages={3629-3641}
}
Poor weather conditions and/or sensor failure always lead to inevitable information loss for remote-sensing images acquired by passive sensor platforms. This common issue makes the interpretation (e.g., target recognition, classification, change detection) of remote-sensing data more difficult. Toward this end, this paper proposes to reconstruct the missing information of optical remote-sensing data by patch matching-based multitemporal group sparse representation (PM-MTGSR). In the framework… CONTINUE READING

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