The matching of SAR and optical images is crucial for various remote sensing applications, such as monitoring natural disasters and change detection. However, the significant differences in geometric and radiometric properties between these two sensors pose challenges for robust and accurate matching. Recent deep learning-based approaches mitigate modality differences by aligning all contents on a single pixel-level feature map, leading to limited robustness to content differences and resolution variations. In this paper, we propose a novel robust SAR-optical matching network to address these challenges. To enhance robustness against noise and resolution changes, we align and match on feature maps of multiple granularities simultaneously. Further, we introduce the novel multi-granularity matching strategy called “look closer to match better” to reduce the computational burden of global matching across multiple granularities. This strategy employs coarse-grained features to quickly narrow down the search range, followed by the use of finer-grained features to gradually locate the finer matching position within a reduced search range, improving both efficiency and performance. Additionally, we address the issue of treating all regions equally during feature alignment by proposing a Non-Shared Contents Filtering (NSCF) module. This module adaptively filters out non-shared regions that are difficult to align, thereby avoiding its interference with the similarity measure of the consistent representation and enhancing robustness to content differences. We evaluate our framework on various satellite datasets. Experiments show that our method achieves the best performance on the SEN1-2 dataset and competitive generalization ability on other unseen satellite datasets.

中文翻译：

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跨多个粒度的共享内容对齐，以实现稳健的 SAR 光学图像匹配

SAR 和光学图像的匹配对于各种遥感应用至关重要，例如监测自然灾害和变化检测。然而，这两个传感器之间几何和辐射特性的显着差异对稳健和准确的匹配提出了挑战。最近基于深度学习的方法通过在单个像素级特征图上对齐所有内容来减轻模态差异，从而导致对内容差异和分辨率变化的鲁棒性有限。在本文中，我们提出了一种新型稳健的 SAR 光学匹配网络来应对这些挑战。为了增强对噪声和分辨率变化的鲁棒性，我们同时在多个粒度的特征图上进行对齐和匹配。此外，我们引入了一种新颖的多粒度匹配策略，称为“看得更近，匹配得更好”，以减少跨多个粒度的全局匹配的计算负担。该策略利用粗粒度特征快速缩小搜索范围，然后利用细粒度特征在缩小的搜索范围内逐步定位更精细的匹配位置，从而提高效率和性能。此外，我们通过提出非共享内容过滤（NSCF）模块来解决特征对齐期间平等对待所有区域的问题。该模块自适应地过滤掉难以对齐的非共享区域，从而避免其对一致表示的相似性度量的干扰，并增强对内容差异的鲁棒性。我们在各种卫星数据集上评估我们的框架。实验表明，我们的方法在 SEN1-2 数据集上实现了最佳性能，并在其他未见过的卫星数据集上实现了有竞争力的泛化能力。