Analysis and Prediction of Regional Land Subsidence with InSAR Technology and Machine Learning Algorithm
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12205-022-1067-4.pdf
Reference35 articles.
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3. Belgiu M, Drăguţ L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114:24–31, DOI: https://doi.org/10.1016/j.isprsjprs.2016.01.011
4. Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing 40(11):2375–2383, DOI: https://doi.org/10.1109/TGRS.2002.803792
5. Casu F, Manzo M, Lanari R (2006) A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data. Remote Sensing of Environment 102(3–4):195–210, DOI: https://doi.org/10.1016/j.rse.2006.01.023
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