Multimodal matching remains a difficult and pressing problem in the imaging processing community. Accurate and robust multimodal matching is important for the performance of applications such as registration and fusion. Traditional image matching algorithms cannot effectively handle multimodal images with severe nonlinear radiometric distortion (NRD). In this paper, a novel Multi-scale Template Matching Algorithm (MSTM) for multimodal image matching is proposed to address this problem. we propose a novel Frequency-domain Convolutional Map (FDCM) based on the wavelet transform and phase congruency (PC) to construct a feature description map that significantly reduces the NRD between multimodal images. The development of omnidirectional aggregated feature vectors with rotational invariance also helped to achieve robustness on rotated images. Finally, a multi-scale template matching strategy improved the matching performance on multimodal images with displacement and scale variations.To improve the time efficiency of the algorithm, most of the complex computations in this paper are performed in the frequency domain. According to the experimental findings on six multimodal image datasets, the method can obtain accurate and robust matching results between multimodal images. Through qualitative and quantitative evaluations, the method outperforms several mainstream multimodal image matching algorithms in terms of matching accuracy, success rate and time consumption.

Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address this problem, we investigated the potential of a deep learning-based method for point symbol recognition and proposed a deep convolutional neural network (DCNN)-based model for this task. We created point symbol datasets from different sources for training and prediction models. Within this framework, atrous spatial pyramid pooling (ASPP) was adopted to handle the recognition difficulty owing to the differences between point symbols and natural objects. To increase the positioning accuracy, the k-means++ clustering method was used to generate anchor boxes that were more suitable for our point symbol datasets. Additionally, to improve the generalization ability of the model, we designed two data augmentation methods to adapt to symbol recognition. Experiments demonstrated that the deep learning method considerably improved the recognition accuracy and efficiency compared with classical algorithms. The introduction of ASPP in the object detection algorithm resulted in higher mean average precision and intersection over union values, indicating a higher recognition accuracy. It is also demonstrated that data augmentation methods can alleviate the cross-domain problem and improve the rotation robustness. This study contributes to the development of algorithms and the evaluation of geographic elements extracted from STMs.

Three-dimensional (3D) scene reconstruction plays an important role in digital cities, virtual reality, and simultaneous localization and mapping (SLAM). In contrast to perspective images, a single panoramic image can contain the complete scene information because of the wide field of view. The extraction and matching of image feature points is a critical and difficult part of 3D scene reconstruction using panoramic images. We attempted to solve this problem using convolutional neural networks (CNNs). Compared with traditional feature extraction and matching algorithms, the SuperPoint (SP) and SuperGlue (SG) algorithms have advantages for handling images with distortions. However, the rich content of panoramic images leads to a significant disadvantage of these algorithms with regard to time loss. To address this problem, we introduce the Improved Cube Projection Model: First, the panoramic image is projected into split-frame perspective images with significant overlap in six directions. Second, the SP and SG algorithms are used to process the six split-frame images in parallel for feature extraction and matching. Finally, matching points are mapped back to the panoramic image through coordinate inverse mapping. Experimental results in multiple environments indicated that the algorithm can not only guarantee the number of feature points extracted and the accuracy of feature point extraction but can also significantly reduce the computation time compared to other commonly used algorithms.