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

发布日期:2016-11-30 09:25:05 阅读次数:[1470]次 作者:

核心提示:来源出版物: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

标题: Patch Matching-Based Multitemporal Group Sparse Representation for the Missing Information Reconstruction of Remote-Sensing Images
作者: Li, XH (Li, Xinghua); Shen, HF (Shen, Huanfeng); Li, HF (Li, Huifang); Zhang, LP (Zhang, Liangpei)
来源出版物: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING  卷: 9  期: 8  特刊: SI  页: 3629-3641  DOI: 10.1109/JSTARS.2016.2533547  出版年: AUG 2016
摘要: 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 of sparse representation, the basic idea is to utilize the local correlations in the temporal domain and the nonlocal correlations in the spatial domain. Based on image patches, the local correlations are first taken into consideration. The similar patches are then grouped for joint sparse representation so that the nonlocal correlations are also considered. Owing to the patch matching of similar patches, the nonlocal correlations in the remote-sensing images are efficiently exploited. Simulated and real-data experiments demonstrate that the proposed method is effective both qualitatively and quantitatively.


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