A modified Goldstein filter for interferogram denoising of interferometric imaging radar altimeter based on multiple...

Area of Science:

• Geoscience
• Remote Sensing
• Signal Processing

Background:

• Signal-to-noise ratio (SNR) decreases with range in radar altimetry.
• Interferometric phase noise degrades data quality, especially in challenging environments like sea ice.

Purpose of the Study:

• Develop an adaptive phase filtering algorithm for imaging radar altimeter data.
• Improve noise reduction while preserving essential interferometric fringe characteristics.

Main Methods:

• Proposed an adaptive phase filtering algorithm combining Goldstein filtering with multiple quality-guided graphs (residue density, pseudo-coherence, pseudo-SNR).
• Determined filter parameters by weighting quality-guided graphs and adjusted window size based on residue count.
• Applied frequency-domain filtering to remove interferometric phase noise.

Main Results:

• The algorithm effectively filters phase noise and preserves edge features in simulated and real-world data (TSX/TDX, airborne radar altimeter).
• Achieved over 86% residue filtering rate on TSX/TDX sea ice data, maintaining sea ice edge characteristics.
• Filtered results closely matched simulated pure interferometric phase sections.

Conclusions:

• The proposed adaptive phase filtering algorithm offers an effective solution for noise reduction in imaging radar altimeter data.
• This method enhances the quality of interferograms, particularly for complex surfaces like sea ice.
• The algorithm successfully balances noise suppression with the preservation of critical data features.