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Objectives: Multi-source image matching is primarily disturbed by nonlinear intensity difference, contrast difference and inconspicuous regional structure features, while the significant differences of texture features result in lack of part structure seriously between light detection and ranging (LiDAR) depth map and aerial image, and this problem causes a mutation in the phase extremum, which further increases the difficulty of matching. Methods: In this paper, a method of efficient matching of LiDAR depth map and aerial image based on phase mean convolution is proposed. In the image feature matching stage, a histogram of phase mean energy convolution (HPMEC) is established, which extended the phase consistency model in order to solve a mean convolution sequence and phase maximum label map by constructing phase mean energy convolution equation. Then the nearest neighbor matching algorithm was completed the initial match and marginalizing sample consensus plus was used to remove outliers. Based on the thread pool parallel strategy, the images were matched by dividing the overlapping grid. Multiple sets of LiDAR depth map and aerial image with different types of ground coverage are used to as dataset to experiment with position scale orientation-scale invariant feature transform (PSO-SIFT), Log-Gabor histogram descriptor (LGHD), radiation-variation insensitive feature transform (RIFT) and histogram of absolute phase consistency gradients (HAPCG) methods respectively. Results: The results show that the performance of HPMEC method is superior to the other four methods in the matching of LiDAR depth map and aerial image, the average running time is 13.3 times of PSO-SIFT, 10.9 times of LGHD, 10.4 times of HAPCG and 7.0 times of RIFT, at the same time the average correct matching points are significantly higher than the other four methods, the root mean square error is lightly better than the other four methods within 1.9 pixels. Conclusions: The proposed HPMEC method could achieve efficient and robust matching between LiDAR depth map and aerial image. [ABSTRACT FROM AUTHOR]

Objectives: Few corresponding points can easily affect the calculation of image pose information, increase the difficulty of constructing a regional network in an aerial triangulation solution, so that lead to problems such as image stitching misalignment, incorrect bundle adjustment results or even failure. In order to better complete the matching of unmanned aerial vehicle (UAV) images, this paper proposes a robust UAV image matching algorithm considering log-polar description and position scale distance. Methods: Firstly, a Gaussian multi-scale image collection is established and feature points are extracted. Secondly, the descriptors are constructed using log-polar coordinates, and a descriptor suitable for UAV image characteristics is established. Then, the feature matching is performed by the distance function of position and scale constraints. Finally, the mode seeking and fast sample consensus method are used to eliminate the outliner and complete the extraction of correspondence. Results: The image obtained by four-rotor UAV is used as the data source, and a comparison experiment of image matching with scale invariant feature transform (SIFT) algorithm and synthetic aperture radar-scale invariant feature transform (SARSIFT) algorithm is carried out. The experimental results show that a 210-dimensional log-polar coordinate descriptor is constructed through the gradient location and orientation histogram. The descriptor can better describe the feature points in 10 directions through the circular neighborhood, making the matching results more robust. The position scale Euclidean distance matching function established by integrating factors such as position and scale can better calculate the UAV image matching relationship, and match more correct corresponding points. In terms of the number of correct corresponding points extracted under the same parameter settings, the proposed algorithm is significantly more than the other two algorithms, and in terms of the root mean square error of the matching results, the algorithm in this article is also significantly better than the two compared algorithms. Conclusions: The proposed algorithm can better extract the corresponding points of UAV images. [ABSTRACT FROM AUTHOR]

Objectives: With the enrichment of heterologous image acquisition methods, heterologous image is widely used in many fields, such as change detection, target recognition and disaster assessment. However, matching is the premise of heterologous image fusion application. Simultaneously, due to the differences in imaging mechanisms of different sensors, heterologous images are more sensitive to differences in illumination, contrast, and nonlinear radiation distortion. Therefore, heterologous image matching still faces some problems. There are two main problems, heterologous image feature detection is difficult due to the difference of imaging mechanism, which indirectly increases the difficulty of matching, heterologous image has significant differences in illumination, contrast and nonlinear radiation distortion, which reduces the robustness of feature description and easily leads to matching failure directly. Methods: This paper proposes a new matching method considering anisotropic weighted moment and the histogram of the absolute phase orientation. Firstly, anisotropic filtering is used for image nonlinear diffusion. Based on this, the maximum moment and minimum moment of image phase consistency are calculated, and the anisotropic weighted moment equation is constructed to obtain the anisotropic weighted moment map. Then, the phase consistency model is extended to establish the absolute phase consistency orientation gradient. Combined with the log polar description template, a histogram of absolute phase consistency gradients (HAPCG) is established. Finally, the Euclidean distance is used as the matching measure for corresponding point recognition. Results: Several groups of heterologous remote sensing images with illumination, contrast, and nonlinear radiation distortion are used as data sources of experiments with scale invariant feature transform (SIFT), position scale orientation -SIFT(PSO -SIFT), Log -Gabor histogram descriptor(LGHD) and radiation variation insensitive feature transform(RIFT) methods, respectively. The results show that HAPCG method is superior to SIFT, PSO-SIFT and LGHD in the comprehensive matching performance of heterologous remote sensing images, and the average matching number of corresponding points is increased by over 2 times, and the root mean square error is 1.83 pixels. When compared with RIFT method, HAPCG method can achieve higher matching accuracy in the case of similar corresponding points and can realize the robust matching of heterologous remote sensing images. Conclusions: The proposed HAPCG method can achieve robust matching performance in heterologous remote sensing images and provide stable data support for multi-source image data fusion and other tasks. [ABSTRACT FROM AUTHOR]

Complex image registration is a crucial step in interferometric synthetic aperture sonar (InSAS) signal processing. The registration quality directly influences the generation of interferogram and subsequent reconstruction of digital elevation model (DEM). Utilizing the geometric model of imaging, the rational function relationship between offset and slant range is deduced, thus this paper propose a new InSAS complex image registration method based on second order rational function surface fitting. Compared with the traditional polynomial surface fitting method, the proposed method in this paper has the advantages of higher precision and smaller calculation cost. The evaluation has been carried out using the root mean squared error(RMSE), correlation coefficient,residue numbers and computing time as the criteria. Simulation and real data experiments results indicate the validity of the proposed method. [ABSTRACT FROM AUTHOR]