A method for stitching remote sensing images with Delaunay triangle feature constraints.

The process of synthesizing multiple images into a seamless panoramic image is referred to as remote sensing image stitching. Existing studies focus less on the influence of topography on the appearance and texture of images and the perturbation of image spectra by topographic changes. This paper presents a remote sensing image stitching method that considers the impact of topography and geomorphology. First, the feature matching was optimized using the Euclidean distance similarity of texture features and the nearest neighbor distance ratio of feature points in remote sensing images as constraints. Then, the Delaunay triangle mesh of feature points in the image overlapping region was constructed, the geometric features of Delaunay triangles were used to optimize the triangle matching and reduce the matching redundancy, and the affine transformation matrix was solved based on the comprehensive consideration of the geometric features of Delaunay triangles and the texture features of the remote sensing images. Finally, the weighted fusion algorithm was applied to stitch and fuse the images. Three image datasets were selected for the experiments, one in which there were large terrain undulations in the imaging regions, one in which the main body of the imaging regions was water, and one in which the overall terrain of the imaging regions had relatively gentle slopes but obscuring features were also present. The results showed that the average correct rates of method feature matching were 89.78%, 94.99%, and 96.17%, which were the best for each algorithm, and the average feature matching times were 4.13, 8.27 and 7.19 s. These times are much lower than those obtained with the APAP and AANAP algorithms and basically the same as those achieved with the SPHP and SURF algorithms. In terms of visual effect, the AG, D Tenengrad , and D Laplacian indices of the proposed method were all significantly improved compared to the APAP, SPHP, AANAP, and SURF algorithms, the advantages were most obvious when dealing with datasets with large topographic relief in the imaging regions, with the maximum improvement of the AG, D Tenengrad , and D Laplacian indices were 85.61%, 95.68%, and 93.12%, respectively. Therefore, it is possible to conclude that the proposed method is more suitable for remote sensing image stitching and fusion of different topographic and geomorphological conditions. [ABSTRACT FROM AUTHOR]