Sub-pixel matching of multimodal optical images is a critical step in combined application of multiple sensors. However structural noise and inconsistencies arising from variations in multimodal image responses usually limit the accuracy of matching. Phase congruency mutual-structure weighted least absolute deviation (PCWLAD) is developed as a coarse-to-fine framework. In the coarse matching stage, we preserve the complete structure and use an enhanced cross-modal similarity criterion to mitigate structural information loss by PC noise filtering. In the fine matching stage, a mutual-structure filtering and weighted least absolute deviation-based is introduced to enhance inter-modal structural consistency and accurately estimate sub-pixel displacements adaptively. Experiments on three multimodal datasets-Landsat visible-infrared, short-range visible-near-infrared, and UAV optical image pairs demonstrate that PCWLAD consistently outperforms eight state-of-the-art methods, achieving an average matching accuracy of approximately 0.4 pixels. The software and datasets are publicly available at https://github.com/huangtaocsu/PCWLAD.

翻译：多模态光学图像的亚像素匹配是多传感器协同应用的关键步骤。然而，由多模态图像响应差异引起的结构噪声与不一致性通常限制了匹配精度。本文提出了相位一致性互结构加权最小绝对偏差（PCWLAD）方法，构建了一种从粗到精的匹配框架。在粗匹配阶段，我们通过相位一致性噪声滤波来保留完整结构，并采用增强的跨模态相似性准则以减少结构信息损失。在精匹配阶段，引入互结构滤波与基于加权最小绝对偏差的匹配策略，以增强模态间的结构一致性，并自适应地精确估计亚像素位移。在三个多模态数据集——Landsat可见光-红外、短程可见光-近红外以及无人机光学图像对上进行的实验表明，PCWLAD方法在性能上持续优于八种先进方法，实现了约0.4像素的平均匹配精度。相关软件与数据集已在https://github.com/huangtaocsu/PCWLAD公开提供。