Time-varying Sea Surface Temperature Reconstruction Leveraging Low Rank and Joint Smoothness Constraints

LI Jiao; WAN Tengwen; QIU Wei

doi:10.11999/JEIT240253

Volume 46 Issue 11

Nov. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(11): 4259-4267

Liu Yang, Li Yu-shan. The Moving Object Detection Based on 2D Spatio-temporal Entropic Thresholding[J]. Journal of Electronics & Information Technology, 2005, 27(1): 39-42.

Citation:

LI Jiao, WAN Tengwen, QIU Wei. Time-varying Sea Surface Temperature Reconstruction Leveraging Low Rank and Joint Smoothness Constraints[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4259-4267. doi: 10.11999/JEIT240253

Liu Yang, Li Yu-shan. The Moving Object Detection Based on 2D Spatio-temporal Entropic Thresholding[J]. Journal of Electronics & Information Technology, 2005, 27(1): 39-42.

Citation:

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Time-varying Sea Surface Temperature Reconstruction Leveraging Low Rank and Joint Smoothness Constraints

doi: 10.11999/JEIT240253

LI Jiao¹,
WAN Tengwen¹,
QIU Wei^{2
,
,}

1.
School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha 410114, China
2.
School of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (42176197), The Natural Science Foundation of Hunan Province (2022JJ40461), The Excellent Youth Foundation of Education Bureau of Hunan Province (21B0301)

Received Date: 2024-04-09
Rev Recd Date: 2024-10-10

Available Online: 2024-10-15

Publish Date: 2024-11-10

Abstract

Abstract

Sea surface temperature is one of the key elements of the marine environment, which is of great significance to the marine dynamic process and air-sea interaction. Buoy is a commonly used method of sea surface temperature observation. However, due to the irregular distribution of buoys in space, the sea surface temperature data collected by buoys also show irregularity. In addition, it is inevitable that sometimes the buoy is out of order, so that the sea surface temperature data collected is incomplete. Therefore, it is of great significance to reconstruct the incomplete irregular sea surface temperature data. In this paper, the sea surface temperature data is established as a time-varying graph signal, and the graph signal processing method is used to solve the problem of missing data reconstruction of sea surface temperature. Firstly, the sea surface temperature reconstruction model is constructed by using the low rank data and the joint variation characteristics of time-domain and graph-domain. Secondly, a time-varying graph signal reconstruction method based on Low Rank and Joint Smoothness (LRJS) constraints is proposed to solve the optimization problem by using the framework of alternating direction multiplier method, and the computational complexity and the theoretical limit of the estimation error of the method are analyzed. Finally, the sea surface temperature data of the South China Sea and the Pacific Ocean are used to evaluate the effectiveness of the method. The results show that the LRJS method proposed in this paper can improve the reconstruction accuracy compared with the existing missing data reconstruction methods.
- Sea surface temperature,
- Time-varying graph signal,
- Signal reconstruction,
- Low rank,
- Joint smoothness

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References(25)

References

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