adugnag/SAR-FCN-DK3

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Python
Created over 4 years ago
Updated over 4 years ago

adugnag/SAR-FCN-DK3

adugnag

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These are a set of functions to apply a fully convolutional network on a multi-temporal SAR images for semantic segmentation.

gee_s1_ard

Creates an analysis ready sentinel-1 SAR image collection in Google Earth Engine by applying additional border noise correction, speckle filtering and radiometric terrain normalization.

deSpeckNet-TF-GEE

This implementation uses python to seamlessly integrate Sentinel-1 SAR image preparation in GEE with deep learning in Tensorflow for SAR image despeckling.

Jupyter Notebook

deSpeckNet

These are a set of scripts to train a deep learning based SAR image despeckling method.

CV-deSpeckNet

This repo implements a complex-valued multi-stream fully convolutional network for PolSAR data despeckling.

S1-S2_Transformer

This implementation uses Google Earth Engine Python API to process Sentinel-1 SAR and Sentinel-2 optical timeseries for tropical dry forest disturbance mapping. It also uses Tensorflow to build a siamese transformer architecture to implicitly learn the seasonality in the two series to make a forest and non-forest inference.

adugnag.github.io

A personal website for Adugna Mullissa