Application of SAR Offset Tracking Based on Dynamic Window Using Local Features for Landslides in Mining Areas

The inability of traditional offset tracking to adapt the feature window to changes in surface feature density limits its effectiveness in monitoring complex surface deformations. This study addresses this issue by proposing a local feature extraction method based on different coordinate grid areas and combining multiscale analysis and multiangle dynamic thresholds. The study uses principal component analysis to select the main variation components for local feature values and dynamically adjust the initial feature window method in conjunction with the variation coefficient. Finally, the study separately employs the structural time series model and spatial autocorrelation to evaluate and compare the performance of the dynamic window and the fixed window, using the offset results to analyze potential causes of landslides. The research indicates that the dynamic window size can effectively capture the complex characteristics of areas with severe deformation and ensure data integrity in landslide edge areas.

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Publication Details

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2024-11-08
DOI
https://doi.org/10.1109/jstars.2024.3492534
Citations
3
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
2.13
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article

Application of SAR Offset Tracking Based on Dynamic Window Using Local Features for Landslides in Mining Areas

Bo Yin, Weixian Tan, Yaolong Qi, Yuejuan Chen et al.
3 citations
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Landslides and related hazards
2.13
article

Application of SAR Offset Tracking Based on Dynamic Window Using Local Features for Landslides in Mining Areas

Bo Yin, Weixian Tan, Yaolong Qi, Yuejuan Chen, Xianglei Li, Pingping Huang, Dejun Zhao, Yang Liu
article en
3 citations

Abstract

The inability of traditional offset tracking to adapt the feature window to changes in surface feature density limits its effectiveness in monitoring complex surface deformations. This study addresses this issue by proposing a local feature extraction method based on different coordinate grid areas and combining multiscale analysis and multiangle dynamic thresholds. The study uses principal component analysis to select the main variation components for local feature values and dynamically adjust the initial feature window method in conjunction with the variation coefficient. Finally, the study separately employs the structural time series model and spatial autocorrelation to evaluate and compare the performance of the dynamic window and the fixed window, using the offset results to analyze potential causes of landslides. The research indicates that the dynamic window size can effectively capture the complex characteristics of areas with severe deformation and ensure data integrity in landslide edge areas.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Inner Mongolia University of Technology (CN)
Climate action
Openalex Percentile: Top 11%
Landslides and related hazards
2.13
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