Code related to the papers :
- M. Muzeau, C. Ren, S. Angelliaume, M. Datcu, and J.P. Ovarlez, “Self-supervised learning based anomaly detection in synthetic aperture radar imaging,” IEEE Open Journal of Signal Processing, pp. 1–9, 2022.
- M. Muzeau, C. Ren, S. Angelliaume, M. Datcu and J.P. Ovarlez, "Self-Supervised SAR Anomaly Detection Guided with RX Detector," 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, pp. 1918-1921
- Pytorch
- Numpy
- Matplotlib
This repository have the adversarial autoencoder with training and prediction phases. It does not include :
- The despeckling algorithm
- The anomaly maps, which is an image of values between 0 and 1, 0 being a "normal" pixel and 1 an "abnormal" one.
- The data it have been trained on for confidentiality reasons
The input data are despeckled images with 4 polarizations. To make the algorithm work the 'norm' parameters have to be adapted to the desired images dynamic.
To train a neural network :
python train.py
To make images reconstruction and compute the change detection :
python predict.py
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