Pattern recognition and correction method for skeleton lines at small patch boundaries

Li, Chengming; Yin, Yong; Wu, Pengda; Wu, Wei

doi:10.1111/tgis.12643

Cited by 6 publications

(11 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, the central axis of the river area is extracted based on Delaunay triangulation, and the result is taken as the primary path. The detailed procedures are illustrated in the papers by Li, Dai, Yin, and Wu (2019) and Li, Yin, Wu, and Wu (2020). Figures 11i,j give the corresponding results.…”

Section: Experiments and Analysesmentioning

confidence: 99%

Automatic recognition method of primary path for an anastomosing river based on its typical features

et al. 2021

Transactions in GIS

Self Cite

View full text Add to dashboard Cite

Primary path recognition is a prerequisite for effective river network generalization. In a river network with a large spatial region, anastomosing rivers refer to those with intertwined river channels. Dense reaches with similar semantic information usually form complex spatial structures, which creates special challenges for accurately delineating the primary path. The recognition of the primary path for anastomosing rivers has not received much attention in the literature. Traditional methods rely on empirical thresholds to determine the basin including the primary path, and do not consider the overall spatial structure features of rivers, which lower the flexibility and accuracy of the primary path recognition. Therefore, this article proposes an automatic recognition method for the primary path of an anastomosing river based on its typical features. First, a directed topology of the river network is constructed to detect the key nodes in forming the anastomosing rivers in the river network, which are the nodes with multiple outflow reaches and are defined as redundant nodes. Second, by considering the flow direction, the effective topological boundary of each redundant node is identified to determine the basin of the anastomosing rivers automatically. Finally, a hierarchical tree is constructed to describe the spatial relationships among redundant nodes and based on the hierarchical tree and other typical features of anastomosing rivers, the optimal connection path between each redundant node and downstream node is calculated to identify the primary path. 1:10,000 topographic river data of the experimental region in Hubei Province, China, were used for validation. The experiment reveals that compared with the state‐of‐the‐art Buttenfield method, the primary path identified by the proposed method is more similar to that obtained manually. The inclination angle difference between the result obtained using the Buttenfield method and the artificial identification result is four times larger than that obtained by the proposed method; moreover, the shape of the primary path detected by the proposed method is more natural and smoother.

show abstract

Section: Experiments and Analysesmentioning

confidence: 99%

Automatic recognition method of primary path for an anastomosing river based on its typical features

et al. 2021

Transactions in GIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…Medial axis transformation [1][2][3]; • Chordal axis transform [4]; • Straight skeleton [5,6]; • Delaunay triangulation [6][7][8][9]; • Splitarea algorithm [10,11]; • Other [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…In successive steps, the selected skeleton edges are modified and removed to improve the applicability of the results. Skeletons are modified with the use of various methods, including correction [7,15], extraction [8], hierarchical feature extraction [16], filtration [17,18] and generalization [19]. Different types of triangles are identified in polygons that are generated with the use of methods based on triangulated irregular networks (TIN) [7,10,20].…”

Section: Introductionmentioning

confidence: 99%

“…Skeletons are modified with the use of various methods, including correction [7,15], extraction [8], hierarchical feature extraction [16], filtration [17,18] and generalization [19]. Different types of triangles are identified in polygons that are generated with the use of methods based on triangulated irregular networks (TIN) [7,10,20]. Different approaches to generating skeleton edges for various triangle types have been proposed in the literature [7,10,20] boundary line.…”

Section: Introductionmentioning

confidence: 99%

“…Different types of triangles are identified in polygons that are generated with the use of methods based on triangulated irregular networks (TIN) [7,10,20]. Different approaches to generating skeleton edges for various triangle types have been proposed in the literature [7,10,20] boundary line. The vertices of a type 3 triangle are located on the polygon boundary line, and none of the triangle's sides overlap the polygon boundary.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Base Point Split Algorithm for Generating Polygon Skeleton Lines on the Example of Lakes

Lewandowicz¹,

Flisek²

2020

IJGI

View full text Add to dashboard Cite

This article presents the Base Point Split (BPSplit) algorithm to generate a complex polygon skeleton based on sets of vector data describing lakes and rivers. A key feature of the BPSplit algorithm is that it is dependent on base points representing the source or mouth of a river or a stream. The input values of base points determine the shape of the resulting skeleton of complex polygons. Various skeletons can be generated with the use of different base points. Base points are applied to divide complex polygon boundaries into segments. Segmentation supports the selection of triangulated irregular network (TIN) edges inside complex polygons. The midpoints of the selected TIN edges constitute a basis for generating a skeleton. The algorithm handles complex polygons with numerous holes, and it accounts for all holes. This article proposes a method for modifying a complex skeleton with numerous holes. In the discussed approach, skeleton edges that do not meet the preset criteria (e.g., that the skeleton is to be located between holes in the center of the polygon) are automatically removed. An algorithm for smoothing zigzag lines was proposed.

show abstract

Skeleton line extraction for areal hydrographic elements considering spatial and hierarchical relationships

Zhang

Qun

Bao

et al. 2022

Geocarto International

View full text Add to dashboard Cite

Pattern recognition and correction method for skeleton lines at small patch boundaries

Cited by 6 publications

References 22 publications

Automatic recognition method of primary path for an anastomosing river based on its typical features

Automatic recognition method of primary path for an anastomosing river based on its typical features

Base Point Split Algorithm for Generating Polygon Skeleton Lines on the Example of Lakes

Skeleton line extraction for areal hydrographic elements considering spatial and hierarchical relationships

Contact Info

Product

Resources

About