Ye Jiaqing, Yu Guorong, Bao Haizhou
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel). 2025 Jul 18;25(14):4472. doi: 10.3390/s25144472.
Multi-modal remote sensing image (MRSI) matching suffers from severe nonlinear radiometric distortions and geometric deformations, and conventional feature-based techniques are generally ineffective. This study proposes a novel and robust MRSI matching method using the side window filter (MSWF). First, a novel side window scale space is constructed based on the side window filter (SWF), which can preserve shared image contours and facilitate the extraction of feature points within this newly defined scale space. Second, noise thresholds in phase congruency (PC) computation are adaptively refined with the Weibull distribution; weighted phase features are then exploited to determine the principal orientation of each point, from which a maximum index map (MIM) descriptor is constructed. Third, coarse position, orientation, and scale information obtained through global matching are employed to estimate image-pair geometry, after which descriptors are recalculated for precise correspondence search. MSWF is benchmarked against eight state-of-the-art multi-modal methods-six hand-crafted (PSO-SIFT, LGHD, RIFT, RIFT2, HAPCG, COFSM) and two learning-based (CMM-Net, RedFeat) methods-on three public datasets. Experiments demonstrate that MSWF consistently achieves the highest number of correct matches (NCM) and the highest rate of correct matches (RCM) while delivering the lowest root mean square error (RMSE), confirming its superiority for challenging MRSI registration tasks.
多模态遥感图像（MRSI）匹配存在严重的非线性辐射畸变和几何变形，传统的基于特征的技术通常效果不佳。本研究提出了一种使用侧窗滤波器（MSWF）的新颖且稳健的MRSI匹配方法。首先，基于侧窗滤波器（SWF）构建了一种新颖的侧窗尺度空间，该空间可以保留共享图像轮廓，并有助于在这个新定义的尺度空间内提取特征点。其次，利用威布尔分布自适应地细化相位一致性（PC）计算中的噪声阈值；然后利用加权相位特征确定每个点的主方向，据此构建最大索引图（MIM）描述符。第三，通过全局匹配获得的粗略位置、方向和尺度信息用于估计图像对几何，之后重新计算描述符以进行精确的对应搜索。在三个公共数据集上，将MSWF与八种先进的多模态方法进行了基准测试，其中包括六种手工制作的方法（PSO-SIFT、LGHD、RIFT、RIFT2、HAPCG、COFSM）和两种基于学习的方法（CMM-Net、RedFeat）。实验表明，MSWF始终能实现最高的正确匹配数（NCM）和最高的正确匹配率（RCM），同时提供最低的均方根误差（RMSE），证实了其在具有挑战性的MRSI配准任务中的优越性。
