2015•Unpublished venueRequires access

Data fusing method of land subsidence monitoring based on leveling and InSAR

Wang Ai-gu

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Abstract

This paper analyzed advantages and disadvantages of leveling and InSAR in land subsidence.Data fusion algorithm and interpolation calculation were proposed.Data fusion method fusing leveling and InSAR can solve disadvantages of lower spatial and temporal resolution in leveling and coherent using InSAR in part of area.Fusing data can better describe the current situation of land subsidence,and better predict trend of land subsidence,further reduce subsidence disaster harm to economic and social.Fusion example about Tianjin showed that the algorithm could solve the shortcomings of single data.Fusing data had the advantages of multiple data,which could provide a rich data to further predict trend of land subsidence.

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What this paper is about

This paper analyzed advantages and disadvantages of leveling and InSAR in land subsidence.Data fusion algorithm and interpolation calculation were proposed.Data fusion method fusing leveling and InSAR can solve disadvantages of lower spatial and temporal resolution in leveling and coherent using InSAR in part of area.Fusing data can better describe the current situation of land subsidence,and better predict trend of land subsidence,further reduce subsidence disaster harm to economic and social.Fusion example about Tianjin showed that the algorithm could solve the shortcomings of single data.Fusing data had the advantages of multiple data,which could provide a rich data to further predict trend of land subsidence.

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Available abstract

This paper analyzed advantages and disadvantages of leveling and InSAR in land subsidence.Data fusion algorithm and interpolation calculation were proposed.Data fusion method fusing leveling and InSAR can solve disadvantages of lower spatial and temporal resolution in leveling and coherent using InSAR in part of area.Fusing data can better describe the current situation of land subsidence,and better predict trend of land subsidence,further reduce subsidence disaster harm to economic and social.Fusion example about Tianjin showed that the algorithm could solve the shortcomings of single data.Fusing data had the advantages of multiple data,which could provide a rich data to further predict trend of land subsidence.

Key concepts: Interferometric synthetic aperture radar, Interpolation (computer graphics), Remote sensing, Sensor fusion, Subsidence, Data mining, Computer science, Environmental science

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