Abstract
Based on the digital surface model (DSM) and jump point search (JPS) algorithm, this study proposed a novel approach to detect the optimal seamline for orthoimage mosaicking. By threshold segmentation, DSM was first identified as ground regions and obstacle regions (e.g., buildings, trees, and cars). Then, the mathematical morphology method was used to make the edge of obstacles more prominent. Subsequently, the processed DSM was considered as a uniform-cost grid map, and the JPS algorithm was improved and employed to search for key jump points in the map. Meanwhile, the jump points would be evaluated according to an optimized function, finally generating a minimum cost path as the optimal seamline. Furthermore, the search strategy was modified to avoid search failure when the search map was completely blocked by obstacles in the search direction. Comparison of the proposed method and the Dijkstra’s algorithm was carried out based on two groups of image data with different characteristics. Results showed the following: (1) the proposed method could detect better seamlines near the centerlines of the overlap regions, crossing far fewer ground objects; (2) the efficiency and resource consumption were greatly improved since the improved JPS algorithm skips many image pixels without them being explicitly evaluated. In general, based on DSM, the proposed method combining threshold segmentation, mathematical morphology, and improved JPS algorithms was helpful for detecting the optimal seamline for orthoimage mosaicking.
Highlights
Image mosaicking is an essential step in production of large-scale digital orthophoto maps (DOM)
Since those obstacle regions were non-continuous in the difference image, it could be found that the seamline still crossed several buildings
This study introduced a novel seamline detection approach based on digital surface model (DSM) and an improved jump point search (JPS) algorithm
Summary
Image mosaicking is an essential step in production of large-scale digital orthophoto maps (DOM). Traditional methods [2,5,8,9,10,11,12,13,14,15] used the per-pixel-based gray information (e.g., grayscale and gradient difference, image edge, color, and texture information) of DOM pair’s overlap to generate a cost map, and different path planning algorithms such as Dijkstra’s algorithm [16], snake model [5], A* algorithm [17], graph cuts [18] and dynamic programming algorithm [19] were applied to determine a minimum cost path as the final seamline. The Dijkstra’s algorithm was first used to detect the seamline in difference images of DOM pairs for mosaicking by Davis [9], while the algorithm complexity would be too high. Yu et al [2] combined image similarity constraints (including information of color, edge, and texture), image saliency and location constraints to generate a total cost image, and determined the optimal seamline with a dynamic programming algorithm
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