In view of the significant intensity difference between remote sensing image pairs, weak robustness, and insufficient key point correspondence, the novel remote sensing image registration method is proposed. Firstly, a nonlinear scale space is established by means of the anisotropic diffusion equation and fast explicit diffusion. Then, an improved gradient calculation method is used to calculate the gradient amplitude of the nonlinear scale-space image to establish the gradient amplitude space of the nonlinear scale space, and the multiscale Harris method is used to detect the feature points in the gradient amplitude space. The experimental results show that this feature extraction method can consider the boundaries and smoothness of objects and reduce the problem of gray-level difference to increase the number of feature points with potential of being correctly matched, and the distribution of feature points is relatively uniform. In addition, the improved gradient calculation method can effectively reduce the impact of nonlinear intensity differences on image registration. Overall, the algorithm can effectively solve the problem of registration difficulties caused by the significant grayscale difference between multisource remote sensing images and enhance the robustness. Compared with other advanced algorithms, this one has higher accuracy and more correct correspondence relations, and the registration performance has been significantly improved.

中文翻译：

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一种新的基于 Harris 特征检测的遥感图像配准

针对遥感影像对之间强度差异显着、鲁棒性弱、关键点对应不充分等问题，提出了一种新颖的遥感影像配准方法。首先，利用各向异性扩散方程和快速显式扩散建立非线性尺度空间。然后，采用改进的梯度计算方法计算非线性尺度空间图像的梯度幅值，建立非线性尺度空间的梯度幅值空间，采用多尺度Harris方法检测梯度幅值空间中的特征点. 实验结果表明，该特征提取方法可以考虑对象的边界和平滑度，减少灰度差异问题，增加有可能被正确匹配的特征点的数量，特征点的分布比较均匀。此外，改进的梯度计算方法可以有效降低非线性强度差异对图像配准的影响。总体而言，该算法能够有效解决多源遥感影像灰度差异显着导致配准困难的问题，增强了鲁棒性。与其他先进算法相比，该算法准确率更高，对应关系更正确，配准性能得到显着提升。