Jin Deng
Chengdu University of Technology
13 Papers
4 Citations
Jin Deng is an academic researcher from Chengdu University of Technology. The author has contributed to research in topics: Interferometric synthetic aperture radar & Computer science. The author has an hindex of 2, co-authored 3 publications.
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Papers
Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR
TL;DR: Wang et al. as discussed by the authors used a Stacking-in-SAR method to identify potential landslides based on a total of 40 Sentinel SAR images acquired from November 2017 to March 2019.
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Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements
Keren Dai,Jin Deng,Qiang Xu,Zhenhong Li,Xianlin Shi,Craig M. Hancock,N. Wen,Lele Zhang,Guanchen Zhuo +8 more
TL;DR: In this article , a wide area potential landslide early identification was carried out using SBAS-InSAR in the whole of Mao County, a mountainous area in western Sichuan (China), with a total of 41 potential landslides successfully detected.
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Monitoring and Predicting the Subsidence of Dalian Jinzhou Bay International Airport, China by Integrating InSAR Observation and Terzaghi Consolidation Theory
TL;DR: In this article , the authors used the Small Baseline Subset Synthetic Aperture Radar (SBAS-InSAR) technology based on Sentinel-1 images from 2017 to 2021 to obtain the subsidence over the land reclamation area of the Dalian Jinzhou Bay International Airport.
Revealing the time lag between slope stability and reservoir water fluctuation from InSAR observations and wavelet tools— a case study in Maoergai Reservoir (China)
TL;DR: Wang et al. as discussed by the authors applied wavelet tools to quantify the time lag between slope stability and reservoir water fluctuation, revealing that the displacement exhibits a seasonal trend, whose high-frequency signal displacement has an interannual period.
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Identifying Potential Landslides in Steep Mountainous Areas Based on Improved Seasonal Interferometry Stacking-InSAR
TL;DR: Wang et al. as discussed by the authors proposed an improved seasonal interferometry stacking-InSAR method, which uses Sentinel-1 satellite data from 2017 to 2022 to identify potential landslides in the mountainous regions of southwest China.
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