distmetrics 0.0.2

Measure Disturbances in OPERA RTC-S1 Stacks

distmetrics

This is a python library for calculating a variety of generic disturbance metrics from input OPERA RTC-S1 time-series including a transformer-based metric proposed in Hardiman-Mostow et al., 2024 [1]. Generic land disturbances refer to any land disturbances observable with OPERA RTC-S1 including land-use changes, natural disasters, deforestation, etc. A disturbance metric is a per-pixel function that quantifies via a radiometric or statistical measures such generic land disturbances between a set of baseline images (pre-images) and a new acquisition (post-image). This library is specific to the dual-polarization VV $+$ VH OPERA RTC-S1 data and will likely need to be modified for other SAR data. The user is expected to provide/curate the co-registered pre-imagery and the post-image for the computation of the distmetric.

Usage

See the notebooks/. These notebooks show how to download the necessary, publicly available time series and calculate these disturbance metrics.

Background

This is a python implementation of disturbance metrics for OPERA RTC-S1 data. The intention is to use this library to quantify disturbance in the RTC imagery. Specifically, our "metrics" define distances between a set of dual polarizations "pre-images" and a single dual polarization "post-image". Some of the metrics only work on single polarization imagery.

The following metrics have been implemented in this library:

1. Transformer metric - mean and std estimated from a Vision Transformer [1] inspired by [2].
2. Mahalanobis 1d and 2d - based on mean and std from sample statistics in patches around each pixel [3], [4].
3. Log-ratio - this is not a non-negative function just a difference of pre and post images in dB [2]. Only works on single polarization images.
4. CuSum metric - both absolute residuals and normalized residuals are computed in a per-pixel fashion. See [5] and [6].

It is worth noting that other metrics can be generated from the above using +, max, min or linear combinations (with positive scalars). As such, when the distmetric has some auxiliary meaning (e.g. as a probability), such combinations are easier as they are more meaningfully comparable.

Installation

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For development

Clone this repository and navigate to it in your terminal. We use the python package manager mamba. We highly recommend mamba and the package repository conda-forge to organize and manage virtual environment required for this library.

1. mamba env update -f environment.yml
2. Activate the environment conda activate distmetrics
3. Install the library with pip via pip install -e . (-e ensures this is editable for development)
4. Install a notebook kernel with python -m ipykernel install --user --name dist-s1.

Python 3.10+ is supported. When using the transformer model, if you have gpu available, it adviseable to check that the output from the below snippet is indeed cuda:

from distmetrics import get_device

get_device() # should be `cuda` if GPU is available or `mps` if using mac M chips

References

[1] H. Hardiman Mostow et al., "Deep Self-Supervised Disturbance Mapping with Sentinel-1 OPERA RTC Synthetic Aperture Radar", in preparation 2024.

[2] O. L. Stephenson et al., "Deep Learning-Based Damage Mapping With InSAR Coherence Time Series," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-17, 2022, Art no. 5207917, doi: 10.1109/TGRS.2021.3084209. https://arxiv.org/abs/2105.11544

[3] E. J. M. Rignot and J. J. van Zyl, "Change detection techniques for ERS-1 SAR data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 31, no. 4, pp. 896-906, July 1993, doi: 10.1109/36.239913. https://ieeexplore.ieee.org/document/239913

[4] Deledalle, CA., Denis, L. & Tupin, F. How to Compare Noisy Patches? Patch Similarity Beyond Gaussian Noise. Int J Comput Vis 99, 86–102 (2012). https://doi.org/10.1007/s11263-012-0519-6. https://inria.hal.science/hal-00672357/

[5] Sarem Seitz, "Probabalistic Cusum for Change Point Detection", https://web.archive.org/web/20240817203837/https://sarem-seitz.com/posts/probabilistic-cusum-for-change-point-detection/, Accessed September 2024.

[6] Tartakovsky, Alexander, Igor Nikiforov, and Michele Basseville. Sequential analysis: Hypothesis testing and changepoint detection. CRC press, 2014.