A Clustering Approach for Atmospheric Phase Error Correction in Ground-Based SAR Using Spatial Autocorrelation.

Area of Science:

• Geophysics
• Remote Sensing
• Earth Observation

Background:

• Ground-based synthetic aperture radar (GB-SAR) is crucial for open-pit mine monitoring.
• Dynamic atmospheric conditions introduce atmospheric phase errors, degrading monitoring accuracy.
• Traditional regression-based atmospheric phase correction (APC) methods are inadequate for complex scenarios.

Purpose of the Study:

• To develop an advanced atmospheric phase correction (APC) method for GB-SAR monitoring.
• To overcome limitations of traditional APC methods in complex atmospheric and terrain conditions.
• To improve the accuracy of deformation monitoring in open-pit mines.

Main Methods:

• Proposed a novel clustering method based on the spatial autocorrelation function for APC.
• Interferograms were divided into blocks, and phase consistency was evaluated using spatial autocorrelation.
• A region growing algorithm classified blocks, followed by merging based on statistical data.

Main Results:

• The proposed clustering method significantly outperformed traditional regression-based APC methods.
• Demonstrated superior performance in complex atmospheric phase scenarios.
• Validated through deformation monitoring of an open-pit mine in Northwest China.

Conclusions:

• The spatial autocorrelation-based clustering method is a superior approach for APC in GB-SAR monitoring.
• The method effectively addresses atmospheric phase errors in challenging environments.
• Enhances the reliability and accuracy of open-pit mine deformation monitoring.