The differences in imaging devices cause multimodal images to have modal differences and geometric distortions, complicating the matching task. Deep learning-based matching methods struggle with multimodal images due to the lack of large annotated multimodal datasets. To address these challenges, we propose XCP-Match based on cross-modality completion pre-training. XCP-Match has two phases. (1) Self-supervised cross-modality completion pre-training based on real multimodal image dataset. We develop a novel pre-training model to learn cross-modal semantic features. The pre-training uses masked image modeling method for cross-modality completion, and introduces an attention-weighted contrastive loss to emphasize matching in overlapping areas. (2) Supervised fine-tuning for multimodal image matching based on the augmented MegaDepth dataset. XCP-Match constructs a complete matching framework to overcome geometric distortions and achieve precise matching. Two-phase training encourages the model to learn deep cross-modal semantic information, improving adaptation to modal differences without needing large annotated datasets. Experiments demonstrate that XCP-Match outperforms existing algorithms on public datasets.

Finding reliable correspondences in two-view image and recovering the camera poses are key problems in photogrammetry and image signal processing. Multilayer perceptron (MLP) has a wide application in two-view correspondence learning for which is good at learning disordered sparse correspondences, but it is susceptible to the dominant outliers and requires additional functional blocks to capture context information. CNN can naturally extract local context information, but it cannot handle disordered data and extract global context and channel information. In order to overcome the shortcomings of MLP and CNN, we design a correspondence learning network based on Transformer, named Vector Rectifier Transformer (VRTNet). Transformer is an encoder-decoder structure which can handle disordered sparse correspondences and output sequences of arbitrary length. Therefore, we design two sub-Transformers in VRTNet to achieve the mutual conversion between disordered and ordered correspondences. The self-attention and cross-attention mechanisms in them allow VRTNet to focus on the global context relations of all correspondences. To capture local context and channel information, we propose rectifier network (including CNN and channel attention block) as the backbone of VRTNet, which avoids the complex design of additional blocks. Rectifier network can correct the errors of ordered correspondences to obtain rectified correspondences. Finally, outliers are removed by comparing original and rectified correspondences. VRTNet performs better than the state-of-the-art methods in the tasks of relative pose estimation, outlier removal and image registration.

Seeking feature correspondences among two or more images is an important problem in computer vision and image processing. The putative matches constructed by the similarity of feature descriptors are often contaminated by many false matches. Typically, the local neighborhood points of a true match point have a rank order, which will be maintained in the corresponding image, and we call it rank consistency. In this paper, we design a number of sorting plans to obtain the neighborhood rank lists by taking full advantage of the local neighborhood geometry structure. In order to measure the differences between rank lists, we adopt the statistically famous Kendall rank correlation coefficient and generalize its definition for matching problem. We design a neighborhood common element guidance strategy and a multi-neighborhood strategy to improve the universality and robustness of our method. Our method has linear complexity and it has superiority over state-of-the-art methods on several challenging data sets. It also performs well in image registration and loop-closure detection tasks. The source code of our method is publicly available athttps://github.com/MnYangs/mGKRCC.

Feature matching is the foundation and key task of remote sensing image registration, which is to establish a reliable point corresponding relationship between the feature points of two images. In this article, a simple and effective local consensus method for rigid and nonrigid feature matching is proposed and applied to solve the problem of high outliers ratio caused by nonrigid transformation, nonlinear radiation difference, and speckle noise in the remote sensing image registration task. We first establish the putative feature correspondences according to the similarity between local descriptors and then use local consensus constraints (including neighborhood consensus and motion vector consensus) to remove outliers. The specific steps are given as follows. First, we use the neighborhood consensus constraint of feature points to carry out preliminary filtering to remove outliers with obvious errors and retain a large number of inliers, so as to obtain a clean reliable set. Then, the reliable set space is grided into several nonoverlapping cells, and the estimated motion vector is calculated for each cell. By taking the comprehensive deviation between the ordinary motion vectors and estimated motion vectors, we transform the matching problem into a mathematical optimization model and derive a closed-form solution with linear time and linear space complexities. In this way, our method can also significantly increase the speed of operation without sacrificing accuracy. A large number of feature matching experiments on remote sensing prove that our method is superior to existing methods and also has good results in the general scene.