BitterSnow

BitterSnow / StaMPS-PyRefactor

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Python refactor of StaMPS for PS-InSAR processing, focused on ISCE/ISCE2 stack preprocessing, HDF5-based Steps 1-8 workflow, snaphu unwrapping, SCLA/SCN correction, and GeoPackage/Shapefile export of velocity and displacement time series. GPL-3.0.

14
1
100% credibility
Found May 25, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

StaMPS-PyRefactor is a Python implementation of the MATLAB StaMPS persistent scatterer InSAR processing chain, designed to measure ground surface deformation from satellite radar images. It takes ISCE/ISCE2 stack outputs, processes them through eight standardized steps (data loading, coherence estimation, PS selection, weeding, phase correction, merging, unwrapping, and atmospheric correction), and exports velocity and displacement time series to standard GIS formats like GeoPackage.

How It Works

1
🛰️ Prepare your satellite radar data

You feed your ISCE-processed satellite radar images into the tool, which automatically organizes them into patches for processing.

2
🔍 Find stable radar reflection points

The tool scans millions of pixels and identifies points where the radar signal bounces back consistently over time—these are your measurement targets.

3
📊 Measure ground motion from the radar signal

The software analyzes how the radar phase changed between image pairs to calculate how much the ground moved at each stable point, down to millimeter precision.

4
🧹 Clean up noisy measurements

The tool removes pixels that are too close together or affected by interference, keeping only the most reliable ground motion measurements.

5
Choose your analysis approach
🏔️
Single area processing

Process one geographic area directly without combining patches

🗺️
Large region analysis

Split the area into overlapping patches, process each, then stitch them together seamlessly

6
📐 Unwrap the phase and estimate errors

The wrapped radar signals are unwrapped to reveal true ground motion, and systematic errors from atmosphere and terrain are modeled and removed.

🗺️ Get your ground motion map

You receive velocity and displacement measurements for millions of points, ready to explore in your favorite mapping software.

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AI-Generated Review

What is StaMPS-PyRefactor?

StaMPS-PyRefactor is a Python port of the MATLAB StaMPS software for Persistent Scatterer Interferometry (PS-InSAR). It takes the original scientific workflow and translates it into native Python, replacing the MATLAB dependency entirely. The project handles the full processing chain from raw ISCE/ISCE2 stack data through to velocity and displacement time series exported as GeoPackage or Shapefile vectors. It uses HDF5 as its native format instead of MATLAB's .mat files, and supports both PS (persistent scatterer) and small-baseline processing modes with snaphu integration for phase unwrapping.

Why is it gaining traction?

This fills a gap for researchers locked out of SAR processing by MATLAB licensing costs. Data scientists working with satellite radar time series can now run the full StaMPS workflow in Python environments without juggling MATLAB licenses or unsupported workarounds. The cross-platform support (Windows and Linux) and HDF5-based architecture make it easier to integrate into modern data pipelines. The validation has been run on real ISCE2 merged stack data producing 1.6 million point features, demonstrating practical usability for production work.

Who should use this?

Geoscientists and remote sensing researchers analyzing land subsidence or infrastructure deformation using Sentinel-1 or similar SAR data. Developers building automated InSAR processing systems who need the StaMPS methodology but cannot use MATLAB. The target audience needs familiarity with ISCE/ISCE2 stack outputs and SAR processing concepts.

Verdict

This is legitimate but immature. With 14 stars and documented gaps in automated test coverage, cross-platform validation, and numerical equivalence guarantees, it needs scrutiny before production use. The documentation honestly flags these limitations which is refreshing. Start with small test datasets, validate outputs against the reference MATLAB implementation, and do not deploy on critical work without independent verification. The 1.0% credibility score reflects the early development stage rather than any fundamental flaw in the approach.

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