  This paper presents three fully convolutional neural network architectures
which perform change detection using a pair of coregistered images. Most
notably, we propose two Siamese extensions of fully convolutional networks
which use heuristics about the current problem to achieve the best results in
our tests on two open change detection datasets, using both RGB and
multispectral images. We show that our system is able to learn from scratch
using annotated change detection images. Our architectures achieve better
performance than previously proposed methods, while being at least 500 times
faster than related systems. This work is a step towards efficient processing
of data from large scale Earth observation systems such as Copernicus or
Landsat.
