distmetrics 1.0.0

Measure Disturbances in OPERA RTC-S1 Stacks

distmetrics

The distmetrics (or informally dist-lib) provides a set of python tools and metrics to identify generic disturbances within OPERA RTC-S1 time-series including a transformer-based metric proposed in Hardiman-Mostow et al., 2024 [1]. The transformer metric and its application occupies most of this library in order to effectively, efficiently apply this deep-learning based model (using a visual transformer architecture). Also, there are:

• tools for despeckling
• GIS tools to merge burst-wise products after metrics have been computed
• downloading burst time series "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 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.

Usage

See the notebooks/. These notebooks show how to:

1. download the necessary, publicly available time series of OPERA RTC-S1 data (see setup below)
2. despeckle the time-series and
3. calculate the disturbance metrics for delineating areas of disturbances

Setup

Please make sure to ensure you have an ASF account (https://search.asf.alaska.edu/#/) and set up a ~/.netrc with:

machine urs.earthdata.nasa.gov
    login <username>
    password <password>

Provenance of Transformer Models

Currently there are 4 models in this library (see from distmetrics.model_load import ALLOWED_MODELS), though you can load your own weights and config assuming the same architecture. The models in this library are:

• transformer_original
• transformer_optimized
• transformer_optimized_fine
• transformer_anniversary_trained
• transformer_anniversary_trained_10 Please see the dist-s1-model [A] for training this transformer model and dist-s1-training-data [B] for curating a dataset from OPERA RTC-S1. There is a link in [A] to the existing dataset that was curated based on time-series with dense preimages and despeckled and masked water. In [B], you can understand how the data is curated. For the models above, here is some discussion:
• transformer_original - the model trained by Harris Hardiman-Mostow on the training data linked to in [A].
• transformer_optimized and transformer_optimized_fine are models with the same architecture as transformer_original and using the same dataset in [A] by dmartinez05 with reduced size. The fine refers to the fact that within the input size of the model (16 x 16) there are patches used within the input size that are 4 x 4 (as opposed to 8 x 8).
• transformer_anniversary_trained and transformer_anniversary_trained_10 were trained by Jungkyo Jung using a dataset similar to one found in [B] using despeckling and some landcover masking. The context length of the the former is 20 and the latter 10.

Background on Metrics

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

We recommend using the conda/mamba package manager to install this library.

mamba install -c conda-forge distmetrics

You can also use pip, although this doesn't ensure proper dependencies are installed.

GPU support

To get the best performance of pytorch, you need to ensure pytorch recognizes the GPU. Using conda-forge distributions, you may require you to ensure that cudatoolkit is installed (this is the additional library in environment_gpu.yml). For our servers, we needed to install cudatoolkit>=11.8 to get pytorch to recognize the GPU. There are certain libraries that may downgrade pytorch to use CPU only (you can check this by looking at the distribution of pytorch before installing the library). There may be different distributions of pytorch and cuda drivers that are compatible, but providing detailed instructions is beyond the scope of these instructions.

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", https://arxiv.org/abs/2501.09129.

[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.