解决多模态遥感图像亚像素匹配中的结构噪声问题，提升配准精度。
A Mutual-Structure Weighted Sub-Pixel Multimodal Optical Remote Sensing Image Matching Method
- 基于相位一致性加权，分粗细两阶段匹配
- 在三组数据集上平均精度达0.4像素
- 适合需要高精度遥感图像融合的科研与应用
多模态光学遥感图像的亚像素匹配是多传感器联合应用的关键步骤。然而，由于多模态图像响应差异带来的结构噪声和不一致性，常限制匹配精度。本文提出一种粗到精框架——相位一致性互结构加权最小绝对偏差（PCWLAD）。粗匹配阶段保留完整结构，通过增强的跨模态相似性准则结合PC噪声滤波，缓解结构信息丢失；细匹配阶段引入互结构滤波与加权最小绝对偏差，提升模态间结构一致性，并自适应估计亚像素位移。在三个多模态数据集——Landsat可见光-红外、短距离可见光-近红外、无人机光学图像对上实验表明，PCWLAD consistently 超越八种先进方法，平均匹配精度约为0.4像素。代码与数据集已公开于 https://github.com/huangtaocsu/PCWLAD。
原文摘要 · Abstract (English)
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.
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