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