This repository contains a collection of interactive and educational Python notebooks designed for teaching and demonstrating key concepts in
- remote sensing, with a focus on image registration, signal processing,
- speckle analysis, dynamic speckle
The notebooks are designed for execution in Google Colab and include reproducible examples based on Sentinel-1 and Sentinel-2 data from Google Earth Engine.
-
Sentinel_2_Cube.ipynb:
Demonstrates how to extract a Sentinel-2 time series using Earth Engine, perform subpixel image registration using phase correlation in the Fourier domain, and visualize geometric misalignments and corrections. -
Generation_Video_Speckle_Funny.ipynb:
Simulates a dynamic speckle sequence based on a synthetic binary text mask, where speckle decorrelation is spatially modulated. The notebook demonstrates how to extract an activity map based on decorrelation rate. -
S1_burst_ASF_to_numpy_VV_VH.ipynb:
provides a lightweight way to explore Sentinel-1 SLC bursts without downloading the entire SAFE package. Thanks to the Alaska Satellite Facility (ASF) API, we can directly request and load burst-level data into Python/Colab, and quickly check what phase information brings to the analysis, by using the color composition: Saturation → Degree of Polarization, Hue → Orientation of the polarization ellipse, Intensity → SPAN (total backscattered power). This notebook does not perform full polarimetry (Sentinel-1 IW is dual-pol only), but these principles are useful to understand what VV/VH combinations can reveal.
You can open each notebook directly in Colab using the badge links below each file, or clone the repository locally.
Click the badge below to open the repo in Google Colab:
