Each SAR satellite generates over 5TB of raw data daily. This guide details the standardized InSAR processing workflow aligned with international benchmarks like ESA's Sentinel-1 mission and NASA-ISRO NISAR system, revealing the technical core of professional-grade processing.
Data Acquisition & Preprocessing: Establishing Interferometric Foundations
Satellite Data Selection Strategy
- Band Matching
C-band (Sentinel-1): Optimal for short-term deformation monitoring.
L-band (ALOS-2): Superior vegetation penetration; X-band (TerraSAR-X): 0.25m resolution. - Spatiotemporal Baseline Optimization
Dijkstra algorithm selects optimal interferometric pairs with vertical baseline <300m (C-band) and compliant temporal intervals.
- Global Data Source Integration
ESA Copernicus: Automated Sentinel-1 SLC downloads
ASF DAAC: ALOS/PALSAR-2 data stream integration
Commercial APIs: On-demand tasking via ICEYE/Capella Space
Precision Orbit Correction
- POE Orbit Refinement
ESA's Precise Orbit Ephemerides (accuracy <5 cm) eliminate satellite positioning errors.
- Baseline Refinement Model
SVD-computed relative orbit parameters control spatial baseline errors within 1%.
- Doppler Centroid Correction
Compensates azimuth spectrum shift for sliding spotlight mode data.
Radiometric Calibration & Noise Suppression
- Absolute Radiometric Calibration
Converts DN values to σ0 backscatter coefficients using corner reflectors or Amazon rainforest targets.
- Multi-looking
4:1 (range:azimuth) ratio balances resolution and noise for enhanced SNR.
- Adaptive Filtering
Goldstein-Werner filter with slope-adjusted strength (optimal 32×32px window).
Core Interferometric Processing: Decoding Phase Information
Interferogram Generation & Flat-Earth Phase Removal
- Complex Data Registration
Cross-correlation method achieves 0.001-pixel subpixel accuracy.
- Flat-Earth Phase Simulation
Calculates theoretical phase using orbital parameters and DEM data (e.g., SRTM 30m).
- Residual Orbit Correction
Polynomial models remove long-wave phase gradients (residual <1 rad).
Phase Unwrapping: From Wrapped to Absolute Deformation
- Minimum Cost Flow Algorithm
Triangulated network for coherence >0.3 areas (error propagation <5%).
- Multi-scale Strategy
Coarse-scale: Low-res deformation trend surface
Fine-scale: Branch-cut method for high-coherence details
AI-enhanced: U-Net model detects phase jumps (3× efficiency gain) - Atmospheric Delay Correction
MERRA-2 meteorological data builds APS models
Spatiotemporal filtering (20km cutoff wavelength)
GNSS fusion improves accuracy to ±1.5mm
Deformation Modeling & Product Generation
Time Series Inversion
1. SBAS Algorithm: Redundant network (15 interferograms/pixel) with SVD solution
2. PS-InSAR: Selects targets (amplitude dispersion <0.25) using phase double-difference model
Geocoding & Validation
Converts to WGS84/UTM projections for ground validation:
1. GNSS cross-validation (R²>0.95)
2. Leveling routes (RMSE ±2.3mm)
3. Monte Carlo error ellipses
Engineering-Grade Outputs
1. Standard Formats
-- GeoTIFF: Deformation rate (mm/yr)
-- CSV: Time series (UTC millisecond precision)
-- KMZ: Google Earth overlays
2. API Services (Beta)
-- RESTful API for threshold alerts
-- Python/Matlab SDKs
"Industrialized InSAR processing is redefining surface monitoring boundaries." As professional global providers, we deliver millimeter accuracy with 100% data traceability for reliable deformation intelligence.