Change detection aims to detect changes of objects or scenes in remote sensing images, which is critical for observing the Earth’s surface. However, due to the insufficient correlation and aggregation of bitemporal features, the existing deep learning methods are still impacted by varied imaging conditions and complicated boundaries of ground objects in high-resolution remote sensing images. To tackle these challenges, we propose a change-aware cascaded dual-decoder network (CACD2Net), which integrates bitemporal features at different levels to facilitate learning change maps from coarse to fine, thus empowering the network to effectively identify changes and refine pixelwise boundaries in a progressive manner. Within the cascaded dual-decoder architecture, the change location decoder utilizes high-level features to generate a coarse change map, which approximates changes’ localization, while the mask refinement decoder further leverages low-level features to create a texture-aware map that captures more texture and structural information about the change regions. By using the coarse change map as guidance and directing the texture-aware map to focus on the details of changes, the boundaries can be gradually refined, ultimately resulting in an accurate change detection mask. We test our model on the season-varying change detection (SVCD) dataset and the Sun Yat-sen University change detection (SYSU-CD) dataset, and the experimental results show that our model surpasses other state-of-the-art change detection methods. Our codes will be available at https://github.com/Moonquakes0/CACD2Net .

中文翻译：

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用于遥感图像变化检测的变化感知级联双解码器网络


变化检测旨在检测遥感图像中物体或场景的变化，这对于观测地球表面至关重要。然而，由于双时态特征的关联和聚合不足，现有的深度学习方法仍然受到高分辨率遥感图像中成像条件变化和地物边界复杂的影响。为了应对这些挑战，我们提出了一种变化感知级联双解码器网络（CACD2Net），它集成了不同级别的双时态特征，以促进学习从粗到细的变化图，从而使网络能够有效地识别变化并细化像素边界渐进的方式。在级联双解码器架构中，变化位置解码器利用高级特征来生成粗略变化图，该图近似变化的定位，而掩模细化解码器进一步利用低级特征来创建纹理感知图来捕获有关变化区域的更多纹理和结构信息。通过使用粗略变化图作为指导，并引导纹理感知图关注变化的细节，可以逐渐细化边界，最终产生准确的变化检测掩模。我们在季节变化变化检测（SVCD）数据集和中山大学变化检测（SYSU-CD）数据集上测试我们的模型，实验结果表明我们的模型超越了其他最先进的变化检测方法。我们的代码将在 https://github.com/Moonquakes0/CACD2Net 上提供。