[IPOL discuss] [IPOL announce] new article: A Deep Learning Model for Change Detection on Satellite Images

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2022/439/


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

Abstract
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.




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