Local Edge Matching for Seamless Adjacent Spatial Datasets with Sequence Alignment

Abstract

This study proposes a local edge matching method with a sequence alignment technique for adjacent spatial datasets. By assuming that the common boundary edges of the datasets are point strings, the proposed method obtains the sequence for point edit operations to align the edges by using the string matching algorithm with the following operations: (1) snapping two points from each string to their average position, (2) removing a point from one string and (3) removing a point from the other string. The costs for these operations are derived from the deformation of the involved line segments in terms of the angle and length changes. The corresponding point pairs are then considered point pairs for which the snapping operation is chosen in a sequence. Based on these pairs, a border area of adjacent spatial datasets can be partitioned into sub-border areas where distinctive matching and alignment processes can be performed.

Detecting Corresponding Vertex Pairs between Planar Tessellation Datasets with Agglomerative Hierarchical Cell-Set Matching

“…Gösseln and Sester [ 3 ] and Butenuth et al [ 2 ] applied the ICP algorithm to vertices extracted from contours of corresponding objects. Recently, Huh et al [ 1 , 4 ] and Wang et al [ 5 ] applied string matching methods to the contours instead of point set matching. Because of separate corresponding point pair detections for corresponding object pairs, these methods can be robust to locally uneven positional discrepancies between datasets [ 1 ].…”

“…In case of CCPs with many small cells, the post-processing of Bel Hadj Ali [ 8 ] suffers from computational expense. Moreover, under the condition of locally uneven positional discrepancies between datasets, these small cells in one dataset can co-intersect substantially different cell-sets in another dataset and present erroneous large CCPs [ 1 ].…”

“…To address these discrepancies, corresponding point pairs are necessary to align one dataset with another. In general, given a point in one dataset, several candidate points in another dataset within a distance threshold are evaluated with similarity measures such as distance, and a single point with the highest similarity is chosen as the corresponding point [ 1 ]. However, these similarities are easily affected by the aforementioned discrepancies.…”

“…However, these similarities are easily affected by the aforementioned discrepancies. Thus, a transformation model, such as an affine or a rigid model, is applied to explain the locally auto-correlated positional discrepancies of each corresponding polygon object pair [ 1 – 5 ].…”