A Deep Learning Model for Change Detection on Satellite Images
Elyes Ouerghi
Elyes Ouerghi, A Deep Learning Model for Change Detection on Satellite Images, Image Processing On Line, 12 (2022), pp. 550–557. https://doi.org/10.5201/ipol.2022.439

Communicated by Jean-Michel Morel
Demo edited by Elyes Ouerghi


Change detection is a classical problem in satellite imaging. The change detection problem aims at the analysis of changes between two images. In this work, we test a deep learning model proposed in 2019 by Caye Daudt et al. on data from the Sentinel-2 satellite with images between 10 m and 60 m of spatial resolution. The model uses the early fusion technique combined with a U-net architecture and can be used on color or multispectral images. The tests are performed on the Onera Satellite Change Detection (OSCD) dataset, which was already used for testing deep learning methods for the change detection problem. Here we propose some experiments to evaluate the performance and limits of the algorithm by Caye Daudt et al.

This is an MLBriefs article, the source code has not been reviewed!
The original source code is available here (last checked 2022/11/26).