By Zhilin Li
With the common use of GIS, multi-scale illustration has turn into a huge factor within the realm of spatial facts dealing with. concentrating on geometric adjustments, this source provides accomplished assurance of the low-level algorithms on hand for the multi-scale representations of other varieties of spatial positive aspects, together with aspect clusters, person strains, a category of strains, person components, and a category of parts. It additionally discusses algorithms for multi-scale illustration of 3-D surfaces and 3-D good points. Containing over 250 illustrations to complement the dialogue, the publication offers the newest learn effects, akin to raster-based paintings, set of rules advancements, snakes, wavelets, and empirical mode decomposition.
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Extra resources for Algorithmic Foundation of Multi-Scale Spatial Representation (2006)(en)(280s)
Zhao, R. L. and Li, Z. , Voronoi-based K-order neighbour relations for spatial analysis, ISPRS Journal of Photogrammetry and Remote Sensing, 59(1-2), 60–72, 2004. Christensen, A. , Line generalization by waterline and medial-axis transformation: success and issues in an implementation of Perkel’s proposal, The Cartographic Journal, 26(1), 19–32, 2000. , A pyramidal data structure for triangle-based surface description, IEEE Computer Graphics and Applications, 9(2), 67–78, 1989. , Chen, X. , and Li, Z.
From Chapter 4 on, algorithms for multi-scale spatial representations will be presented. 4. Chapter 4 is dedicated to the multi-scale representation of point features. The elimination of individual point features is an easy operation and there is no need of any algorithm. The displacement of a point feature is similar to displacement of a line or an area feature and will be discussed in Chapter 11, which is dedicated to the topic of displacement. The magnification of a point feature means the enlargement of a small area feature and will be discussed in Chapter 9.
In this table, a point is represented by P(x,y); the term distance in rows 1–3 means the shortest Euclidean distance; the α in row 4 is the slope angle; the ω in row 6 is the angle opposite the side c. In row 7 the N points that form an area should be arranged in a clockwise direction, and the (N + 1)th point means the first point. 2) The unit of d(P1,P2) is a pixel. 414) pixels. This result in decimal form is inconvenient to use in raster; a distance in integer numbers is more desirable and thus normally employed.
Algorithmic Foundation of Multi-Scale Spatial Representation (2006)(en)(280s) by Zhilin Li