研究目的
To develop an effective and efficient deformation estimation method for time series InSAR processing that overcomes limitations of existing algorithms, such as high computing costs and errors from local extremums, using a simulated annealing-based approach.
研究成果
The SA-based deformation estimation algorithm is effective, efficient, and reliable for TSInSAR processing, overcoming local extremum issues and providing high-precision results with reduced computation time. It is suitable for monitoring surface deformations and can be integrated into standard InSAR workflows.
研究不足
The algorithm may still have computational demands for very large datasets, and its performance could be affected by the choice of parameters like iteration length and cooling factor. It relies on the accuracy of phase measurements and external DEM, and may not handle all types of noise or deformation models perfectly.
1：Experimental Design and Method Selection:
The study proposes a novel deformation estimation method based on the simulated annealing (SA) algorithm for TSInSAR processing. It involves designing an optimization framework to estimate deformation parameters and height from differential interferometric phases, with improvements in the annealing process for better efficiency and accuracy.
2：Sample Selection and Data Sources:
For simulation, a quadratic function model with Gaussian noise is used. For real data, 17 TerraSAR-X images of the Beijing area from 2011-2012 are employed, along with leveling data for validation.
3：List of Experimental Equipment and Materials:
SAR images (TerraSAR-X), leveling data, MATLAB software for implementation, and computational resources for processing.
4：Experimental Procedures and Operational Workflow:
Standard PSI processing is followed: select PS points, generate differential interferograms, remove topographic and flattened phases using external DEM, construct PS measurement network, apply SA algorithm for deformation estimation (with iterative steps, cooling schedule, and acceptance criterion), and validate with leveling data.
5：Data Analysis Methods:
The object function is the residual phase temporal coherent coefficient. Performance is evaluated through simulation with noise levels and comparison with ILS algorithm in terms of accuracy and runtime, and real data is compared with leveling measurements using fitting and visualization.
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