Yao Yongxiang, Zhang Yongjun, Wan Yi, Liu Xinyi, Yan Xiaohu, Li Jiayuan
IEEE Trans Image Process. 2022;31:2584-2597. doi: 10.1109/TIP.2022.3157450. Epub 2022 Mar 21.
Traditional image feature matching methods cannot obtain satisfactory results for multi-modal remote sensing images (MRSIs) in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences (NRD) and complicated geometric distortion. The key to MRSI matching is trying to weakening or eliminating the NRD and extract more edge features. This paper introduces a new robust MRSI matching method based on co-occurrence filter (CoF) space matching (CoFSM). Our algorithm has three steps: (1) a new co-occurrence scale space based on CoF is constructed, and the feature points in the new scale space are extracted by the optimized image gradient; (2) the gradient location and orientation histogram algorithm is used to construct a 152-dimensional log-polar descriptor, which makes the multi-modal image description more robust; and (3) a position-optimized Euclidean distance function is established, which is used to calculate the displacement error of the feature points in the horizontal and vertical directions to optimize the matching distance function. The optimization results then are rematched, and the outliers are eliminated using a fast sample consensus algorithm. We performed comparison experiments on our CoFSM method with the scale-invariant feature transform (SIFT), upright-SIFT, PSO-SIFT, and radiation-variation insensitive feature transform (RIFT) methods using a multi-modal image dataset. The algorithms of each method were comprehensively evaluated both qualitatively and quantitatively. Our experimental results show that our proposed CoFSM method can obtain satisfactory results both in the number of corresponding points and the accuracy of its root mean square error. The average number of obtained matches is namely 489.52 of CoFSM, and 412.52 of RIFT. As mentioned earlier, the matching effect of the proposed method was significantly greater than the three state-of-art methods. Our proposed CoFSM method achieved good effectiveness and robustness. Executable programs of CoFSM and MRSI datasets are published: https://skyearth.org/publication/project/CoFSM/.
传统的图像特征匹配方法在大多数情况下无法为多模态遥感图像（MRSI）获得令人满意的结果，因为不同的成像机制会带来显著的非线性辐射畸变差异（NRD）和复杂的几何畸变。MRSI匹配的关键在于试图减弱或消除NRD并提取更多边缘特征。本文介绍了一种基于共生滤波器（CoF）空间匹配（CoFSM）的新型鲁棒MRSI匹配方法。我们的算法有三个步骤：（1）基于CoF构建一个新的共生尺度空间，并通过优化的图像梯度提取新尺度空间中的特征点；（2）使用梯度位置和方向直方图算法构建一个152维的对数极坐标描述符，这使得多模态图像描述更加鲁棒；（3）建立一个位置优化的欧几里得距离函数，用于计算特征点在水平和垂直方向上的位移误差，以优化匹配距离函数。然后对优化结果进行重新匹配，并使用快速样本一致性算法消除异常值。我们使用多模态图像数据集对我们的CoFSM方法与尺度不变特征变换（SIFT）、直立SIFT、粒子群优化SIFT（PSO-SIFT）和辐射变化不敏感特征变换（RIFT）方法进行了比较实验。对每种方法的算法进行了定性和定量的综合评估。我们的实验结果表明，我们提出的CoFSM方法在对应点数及其均方根误差的准确性方面都能获得令人满意的结果。CoFSM获得的平均匹配数为489.52，RIFT为412.52。如前所述，所提方法的匹配效果明显优于三种现有方法。我们提出的CoFSM方法具有良好的有效性和鲁棒性。CoFSM的可执行程序和MRSI数据集已发布：https://skyearth.org/publication/project/CoFSM/ 。
