期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 56, 期 6, 页码 3047-3061出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2790262
关键词
Missing information reconstruction; multitemporal remotely sensed images; tensor completion
类别
资金
- NSFC [61772003, 61402082]
- Japan Society for the Promotion of Science under Grant KAKENHI [15K20955]
- Alexander von Humboldt Fellowship
- European Research Council through the European Union's Horizon Research and Innovation Program [ERC-2016-StG-714087]
- Helmholtz Association through the Framework of the Young Investigators Group SiPEO [VH-NG-1018]
- Grants-in-Aid for Scientific Research [15K20955] Funding Source: KAKEN
Remotely sensed images may contain some missing areas because of poor weather conditions and sensor failure. Information of those areas may play an important role in the interpretation of multitemporal remotely sensed data. This paper aims at reconstructing the missing information by a nonlocal low-rank tensor completion method. First, nonlocal correlations in the spatial domain are taken into account by searching and grouping similar image patches in a large search window. Then, low rankness of the identified fourth-order tensor groups is promoted to consider their correlations in spatial, spectral, and temporal domains, while reconstructing the underlying patterns. Experimental results on simulated and real data demonstrate that the proposed method is effective both qualitatively and quantitatively. In addition, the proposed method is computationally efficient compared with other patch-based methods such as the recently proposed patch matching-based multitemporal group sparse representation method.
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