  In the remote sensing field, Change Detection (CD) aims to identify and
localize the changed regions from dual-phase images over the same places.
Recently, it has achieved great progress with the advances of deep learning.
However, current methods generally deliver incomplete CD regions and irregular
CD boundaries due to the limited representation ability of the extracted visual
features. To relieve these issues, in this work we propose a novel
Transformer-based learning framework named TransY-Net for remote sensing image
CD, which improves the feature extraction from a global view and combines
multi-level visual features in a pyramid manner. More specifically, the
proposed framework first utilizes the advantages of Transformers in long-range
dependency modeling. It can help to learn more discriminative global-level
features and obtain complete CD regions. Then, we introduce a novel pyramid
structure to aggregate multi-level visual features from Transformers for
feature enhancement. The pyramid structure grafted with a Progressive Attention
Module (PAM) can improve the feature representation ability with additional
inter-dependencies through spatial and channel attentions. Finally, to better
train the whole framework, we utilize the deeply-supervised learning with
multiple boundary-aware loss functions. Extensive experiments demonstrate that
our proposed method achieves a new state-of-the-art performance on four optical
and two SAR image CD benchmarks. The source code is released at
https://github.com/Drchip61/TransYNet.
