ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001
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MULTIRESOLUTION TERRIAN MODEL
Jin ZHANG
Dep. Of Surveying and Mapping
Taiyuan University of Technology (Middle Campus) 030024
Zhangjingis@yeah.net
KEY WORDS: DEM Multiresolution LOD GIS
ABSTRACT: DEM have been studied in the GIS literature for a long time. They have become a major constituent part of geographic
information processing in the earth and engineering sciences. We use DEM to represent the terrain in GIS. The more data are available,
the better representations of a terrain can be built. But not all tasks in the framework of a given application necessarily require the same
accuracy, and even a single task may need different levels of accuracy in different areas of the domain. For instance, in landscape
visualization, or in flight simulation, the terrain must be rendered at different degrees of resolution depending on their distance from the
viewpoint. Multiresolution models, such as LOD, offer the possibility of representing and analyzing a terrain at a range of different levels
of detail. Many papers have published in LOD studying. But in GIS, the most important issue or the key problem, such as combining
different multiresolution spatial data into single representation and for tiled terrain how to solve tile to tile edge matching in
multiresolution model and how to fully consider the terrain feature in multiresolution TIN model, has not been solved so good till today.
The situation in LOD or Multiresolution model first overviewed in this paper. Then we pay more attention in how to fully consider the
terrain feature in multiresolution TIN model and how to solve the tile-to -tile edge matching.
1. INTRODUCTION
(DTMs), sometimes called (DEMs), have been studied in the
GIS literature for a long time. They have become a major
constituent part of geographic information processing in the
earth and engineering sciences. DTMs require specific input
data, data models and algorithms that are different from those
needed to represent and process 2D data. On the other hand,
the activity of modeling and processing digital terrain data
must be regarded as a component of a GIS which needs to be
clearly linked to other processing functions of a spatial
database.
Elevation data are acquired either directly on a terrain through
sampling techniques, or through the digitization of existing
elevation maps (contours). Raw data come in the form of
points either regularly distributed or scattered on a two
dimensional domain; sometimes points are arranged to form
polygonal lines, approximating either lineal features of the
terrain, or contour lines. On the basis of such data, different
models can be built, depending on the needs of specific
applications: contour maps, raster data models on dense grids,
approximations of the surface over the whole domain. Surface
models can be defined either piecewise on a partition of the
domain (either regular or irregular), or through a single
polynomial function of high order.
A topographical surface (or terrian) T can be defines as the
image of a real bivariate function f defined over a domain D in
the Euclidean plane, i.e., T={( x,y,f(x,y ))| ( x,y)e D}. Given a
real value q, the set C„ (q ) = {(x,y ) e D | f(x,y )=q} is the set
of contours T of at elevation q. If q is not an extreme value of f,
then Co (q ) is a set of simple nonintersecting lines, which are
either closed or open with endpoints on the boundary of
domain D .
The more data are available, the better representations of a
terrain can be built. Modern acquisition techniques provide
huge datasets that permit to achieve high accuracy, but this is
paid high costs for storage and processing. On the other hand,
not all tasks in the framework of a given application
necessarily require the same accuracy, and even a single task
may need different levels of accuracy in different areas of the
domain. For instance, in landscape visualization, or in flight
simulation, the terrain must be rendered at different degrees of
resolution depending on their distance from the viewpoint. The
concept of multiresolution terrain model is generically related
to the possibility of using different representations of a
geometric object (e.g., a terrain), having different levels of
accuracy and complexity in order to optimize the tradeoff
between accuracy, and cost of data storage and processing.
Approximate models represent a terrain at a predefined
reduced LOD; multiresolution models offer the possibility of
representing and analyzing a terrain at a range of different
levels of detail.
So what is needed is a hierarchy of representations at various
levels of detail; this makes it possible for a given view point to
render the terrain at an adequate level of detail. Subsequent
levels of the hierarchy should not differ too much in
appearance, switching to more and more detailed
representations when zooming in (this happens for instance in
flight simulation, when one is landing) should not cause
disturbing “jumps" in the image. On the other hand, the
reduction of the number of triangles in subsequent levels
should not be too small, otherwise too many levels would be
needed, resulting in an unacceptable increase in storage. It is
in general insufficient for rendering purposes to use only one
level of detail at a time; although some part of the terrain may
be close to the view point, another part (the horizon, for
example) can still be far away. Hence, one would like to
combine parts from different levels into a single representation
of the terrain, such that each part of the terrain is rendered
with appropriate detail. This means that the levels cannot be
completely independent, as it should be possible to glue them
together smoothly.
The author studys these issue in the following. We first
overview the situation of multiresolution terrain model or LOD.
Then we primary study three main methods, including:
Hierarchical TIN, Adding or deleting point based Delaunay
rules and Hierarchical Dynamic Simplification methods. These
methods have its advantage and disadvantage. Main problems
may be consistency between adjancet edges and tile edges in
HTIN model. The other problems are how to divide spatial area
and code it and how to fully consider the terrain feature in
adding and deleting points based on Delaunay rule
retriangulating. We introduce the HOOM and technology of
tile-to-tile edge match solve these key problem.
2. THE SITUATION OF SURFACE SIMPLIFICATION: A
SURVEY OF TRADITIONAL LEVEL-OF-DETAIL
ALGORITHMS
Polygonal simplification is a very old topic in computer
graphics. As early as 1976 James Clark described the benefits
of representing objects within a scene at several resolutions,
and flight simulators have long used hand-crafted
multi-resolution models of airplanes to guarantee a constant
frame rate [Clark, 76 and Cosman, 81]. Recent years have
seen a flurry of research into generating such multi-resolution
representations of objects automatically by simplifying the
polygonal geometry of the object.
Note that terrains, or tessellated height fields, are a special
category of polygonal models. A bewildering variety of
simplification techniques have appeared in the recent literature;
this section attempts to classify the important similarities and
differences among these techniques.
