A Coherence Improvement Method Based on Sub-Aperture InSAR for Human Activity Detection

Area of Science:

• Geophysics
• Remote Sensing Technology

Background:

• Human activity detection is crucial for social security monitoring.
• Detecting subtle human activity requires high coherence in Interferometric Synthetic Aperture Radar (InSAR) data.
• Repeat-pass InSAR is a key technique for identifying potential activity between image acquisitions.

Purpose of the Study:

• To present a novel coherence improvement approach using sub-aperture InSAR for human activity detection.
• To enhance the detection of subtle human movements by improving SAR image coherence.

Main Methods:

• A coherence improvement approach based on sub-aperture Interferometric Synthetic Aperture Radar (InSAR).
• Integration of sub-aperture interferometric results to enhance coherence.
• Utilizing airborne Ka-band frequency modulated continuous wave (FMCW) InSAR data for validation.

Main Results:

• The proposed sub-aperture InSAR method effectively improves coherence between two complex SAR images.
• Demonstrated ability to significantly enhance the detection of human activity.
• Experimental validation using actual airborne Ka-band FMCW InSAR data.

Conclusions:

• The sub-aperture InSAR technique offers a viable solution for improving coherence in SAR imagery.
• This method provides a reliable means for detecting human activity of interest.
• The approach is validated by experimental results from Ka-band FMCW InSAR data.