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.

本文提出了具有核对齐图像对的三种全卷积神经网络结构来执行变化检测，其中最显著的是，我们提出了两个完全卷积网络的连体扩展，利用当前问题的启发式规则在我们对两个开放变化检测数据集、使用 RGB 和多光谱图像的测试中取得了最佳结果。我们展示了我们的系统能够使用注释变化检测图像从头开始学习，我们的架构表现优于先前提出的方法，同时比相关系统快至少 500 倍。这项工作是朝着有效处理大规模地球观测系统数据的一步。