Seamline Determination for High Resolution Orthoimage Mosaicking Using Watershed Segmentation

Wang, Mi; Yuan, Shenggu; Pan, Jun; Fang, Liuyang; Zhou, Qinghua; Yang, Guopeng

doi:10.14358/pers.82.2.121

Cited by 17 publications

(18 citation statements)

References 22 publications

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“…() and Wang et al. () adopted the image segmentation method to exclude, as far as possible, areas with buildings and then performed a pixel‐level optimisation to refine the seamline using the Dijkstra (shortest path) algorithm. Pan et al.…”

Section: Introductionmentioning

confidence: 99%

“…Pan et al (2015) introduced the region change-detection method to determine buildings within an ortho-image, and then optimise the seamline excluding these areas of change between neighbouring images. Pan et al (2014) and Wang et al (2016) adopted the image segmentation method to exclude, as far as possible, areas with buildings and then performed a pixel-level optimisation to refine the seamline using the Dijkstra (shortest path) algorithm. Pan et al (2017) used a morphological building index and a normalised difference vegetation index to extract buildings and vegetation in the overlaps of adjacent images to avoid seamlines passing through these features.…”

Section: Introductionmentioning

confidence: 99%

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Seamline optimisation for urban aerial ortho‐image mosaicking using graph cuts

Zhang

et al. 2018

The Photogrammetric Record

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Optimal seamline detection is a key step when composing digital orthophotomaps (DOM) of extensive areas from overlapping ortho‐images. To avoid seamlines passing through buildings when mosaicking, seamline detection between adjacent ortho‐images is casted as a graph‐cut problem. To avoid buildings, colour differences and gradient magnitude derived from the ortho‐images, together with approximate object heights derived from a digital surface model (DSM), are combined for constructing the cost term of the graph. To efficiently obtain an approximate height value for each DOM unit, an expanded normalised DSM is proposed. Furthermore, the designed cost term for final composite ortho‐image merging is weighted to prioritise pixels closest to the nadir point. Experiments with strips of aerial images show that the proposed method obtains seamless mosaicking of ortho‐images without manual work.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seamline optimisation for urban aerial ortho‐image mosaicking using graph cuts

Zhang

et al. 2018

The Photogrammetric Record

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“…This process is implemented in many applications, e.g., environmental monitoring and disaster management (Díaz-Varela et al, 2015;Li and Shao, 2014;Pan et al, 2014a). For mosaicking orthoimages (referred to as "images" from here on), the seam-based mosaicking method is commonly used (Ai et al, 2011;Mills and McLeod, 2013;Pan et al, 2009;Pan et al, 2014b;Soille, 2006;Wang et al, 2016). In this method, the seams between the images to be mosaicked must be determined in such a way as to minimize the visual transition.…”

Section: Introductionmentioning

confidence: 99%

Using vector building maps to aid in generating seams for low-attitude aerial orthoimage mosaicking: Advantages in avoiding the crossing of buildings

Wang

Cao

Xin

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

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Abstract:A novel seam detection approach based on vector building maps is presented for lowattitude aerial orthoimage mosaicking. The approach tracks the centerlines between vector buildings to generate the candidate seams that avoid crossing buildings existing in maps. The candidate seams are then refined by considering their surrounding pixels to minimize the visual transition between the images to be mosaicked. After the refinement of the candidate seams, the final seams further bypass most of the buildings that are not updated into vector maps. Finally, three groups of aerial imagery from different urban densities are employed to test the proposed approach. The experimental results illustrate the advantages of the proposed approach in avoiding the crossing of buildings. The computational efficiency of the proposed approach is also significantly higher than that of Dijkstra's algorithm.

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“…Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency.ideal seamline should avoid such objects [1,[4][5][6][7][8]. A seamline is the line along which overlapping areas will be mosaicked.…”

mentioning

confidence: 99%

“…In this paper, our work focuses on automatic seamline determination for urban image mosaicking.In order to minimize the transition of the final mosaic image, the ideal seamline should avoid crossing obvious objects as much as possible and go along the objects which have small relief displacement. Differential expression is essential for seamline determination, which is a measure of the difference between left and right image overlap areas [1,7]. The method based on differential expression is the basic method of seamline optimization.…”

mentioning

confidence: 99%

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network

Yuan¹,

Yang

Li³

et al. 2020

Sensors

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Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic mosaicking of orthoimages. The seamlines that follow the centerlines of roads where no significant differences exist are beneficial to improve the quality of image mosaicking. Based on this idea, this paper proposes a novel method of seamline determination based on road probability map from the D-LinkNet neural network for urban image mosaicking. This method optimizes the seamlines at both the semantic and pixel level as follows. First, the road probability map is obtained with the D-LinkNet neural network and related post processing. Second, the preferred road areas (PRAs) are determined by binarizing the road probability map of the overlapping area in the left and right image. The PRAs are the priority areas in which the seamlines cross. Finally, the final seamlines are determined by Dijkstra’s shortest path algorithm implemented with binary min-heap at the pixel level. The experimental results of three group data sets show the advantages of the proposed method. Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency.

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Seamline Determination for High Resolution Orthoimage Mosaicking Using Watershed Segmentation

Cited by 17 publications

References 22 publications

Seamline optimisation for urban aerial ortho‐image mosaicking using graph cuts

Seamline optimisation for urban aerial ortho‐image mosaicking using graph cuts

Using vector building maps to aid in generating seams for low-attitude aerial orthoimage mosaicking: Advantages in avoiding the crossing of buildings

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network

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