Explicit Change Relation Learning for Change Detection in VHR Remote
  Sensing Images
Dalong Zheng Zebin Wu Jia Liu Chih-Cheng Hung Zhihui Wei
Change detection has always been a concerned task in the interpretation of
remote sensing images. It is essentially a unique binary classification task
with two inputs, and there is a change relationship between these two inputs.
At present, the mining of change relationship features is usually implicit in
the network architectures that contain single-branch or two-branch encoders.
However, due to the lack of artificial prior design for change relationship
features, these networks cannot learn enough change semantic information and
lose more accurate change detection performance. So we propose a network
architecture NAME for the explicit mining of change relation features. In our
opinion, the change features of change detection should be divided into
pre-changed image features, post-changed image features and change relation
features. In order to fully mine these three kinds of change features, we
propose the triple branch network combining the transformer and convolutional
neural network (CNN) to extract and fuse these change features from two
perspectives of global information and local information, respectively. In
addition, we design the continuous change relation (CCR) branch to further
obtain the continuous and detail change relation features to improve the change
discrimination capability of the model. The experimental results show that our
network performs better, in terms of F1, IoU, and OA, than those of the
existing advanced networks for change detection on four public very
high-resolution (VHR) remote sensing datasets. Our source code is available at
https://github.com/DalongZ/NAME.