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.

翻译：在遥感领域，变化检测旨在从同一区域的双时相图像中识别并定位变化区域。近年来，随着深度学习的进展，这一领域取得了显著成果。然而，当前方法由于提取的视觉特征表征能力有限，通常导致检测区域不完整且边界不规则。为解决这些问题，本文提出一种基于Transformer的新型学习框架TransY-Net，用于遥感图像变化检测。该框架从全局视角改进特征提取，并以金字塔方式融合多层级视觉特征。具体而言，所提框架首先利用Transformer在长距离依赖建模中的优势，有助于学习更具判别性的全局特征并获得完整的变化检测区域。随后，我们引入一种新型金字塔结构，用于聚合Transformer的多层级视觉特征以增强特征表示。该金字塔结构结合渐进式注意力模块，通过空间注意力和通道注意力增强特征表示能力并引入额外跨维度依赖关系。最后，为更好地训练整体框架，我们采用多重边界感知损失函数的深度监督学习策略。大量实验表明，所提方法在四个光学和两个SAR图像变化检测基准上达到了新的最优性能。源代码已发布于https://github.com/Drchip61/TransYNet。