Imaging with different spectrums often leads to significant nonlinear differences in the intensity. Consequently, automatic matching of the multispectral images is a challenging task. Considering the limitations of using intensity information alone, this paper studies multispectral image matching based on the structure consistency (MMSC). Firstly, the Log-Gabor filter is extended to construct the phase congruency maps, which embody the structural properties of images and provide rich meaningful features. Then, a new algorithm is developed for salient feature point detection by performing nonlinear diffusion on the phase congruency. Those feature points reflect the characteristics of image structure which are illumination and contrast invariant. Finally, structural descriptors are built according to the orientation of the maximum Log-Gabor filter responses, and the matching can be performed by computing the correspondent descriptors. To evaluate the effectiveness of the proposed method, extensive experiments are carried out on three multispectral image datasets. The experimental results show that, the proposed method is not sensitive to the nonlinear intensity variance across spectrums, and outperforms the comparison methods in terms of the number of correct matches and the matching precision.

The goal of image stitching is to generate high-quality panoramic images with minimal computational cost. However, variations in viewpoint or scene depth can cause parallax effects in UAV images, complicating precise alignment and leading to artifacts such as ghosting, blurring, and misalignment. While advanced seamline detection algorithms reduce ghosting and blurring, structural distortions and misalignments near the seamline often remain, negatively affecting stitching quality. Moreover, these algorithms typically face challenges in balancing computational efficiency with alignment accuracy. In this paper, we propose a robust and flexible UAV image stitching method based on global optimal seamline detection and local alignment with seamline constraint. Our approach ensures precise alignment while maintaining processing efficiency. First, a global transformation-based alignment algorithm is used to pre-align the images to a common coordinate system. Then, an efficient weighted fast sweeping (WFS) algorithm is proposed to detect the globally optimal seamline, minimizing artifacts in overlapping regions caused by alignment errors and dynamic objects. Finally, an optical flow-guided local alignment method with seamline constraint is developed to correct residual misalignments along the seamline, reducing global structural distortion. Extensive experiments on a range of challenging datasets demonstrate that the proposed method outperforms existing approaches, producing more natural-looking stitching results.

Co-registration of multimodal remote sensing images (MRSI) remains a challenging task due to nonlinear radiometric differences (NRD) and geometric distortions. This letter proposes a robust registration method based on steerable Hermite filters and geometric constraints. The method consists of three stages: coarse registration, image matching, and fine registration. In the first stage, geo-referencing information is used to partition the overlapping region into sub-blocks for initial alignment. In the second stage, steerable Hermite filters are applied to extract multi-scale and multi-orientation structural features, which are fused into a principal moment map to detect highly repeatable feature points. A structural descriptor is subsequently constructed from an orientation index map generated through hierarchical pooling, enabling robust matching under NRD. In the final stage, an iterative optimization strategy incorporating geometric constraints is employed to eliminate mismatches and refine the registration. Experimental results on multiple MRSI datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both matching robustness and registration accuracy.