KEYWORDS: Linear filtering, Data modeling, Fuzzy logic, Tin, Cartography, Raster graphics, Classification systems, Lithium, Reliability, Geographic information systems
Since different applications have different requests, an already generated DEM does not always suit all the demands of
an application. The objective of DEM generalization is the generation of a smooth surface with a lower resolution than
the original data, which should preserve topographic features and be appropriate for a smaller scale relief presentation. In
this paper, a method of DEM generalization based on analysis of local geometry and landscape context is proposed.
Through three kinds of filtering: low-pass filter, threshold-area-based filter and smoothing filter, the approach get four
steps to generalize the DEM with lower resolution by an iterative (hierarchical) procedure. In this procedure, analysis of
the fundamental landforms is based on a combination of slope gradient and curvature. A case study is put forward to give
the detail explanation. Meantime, its reliability is described from elevation range and contours derived from generalized
DEMs.
In this paper, a method to generate a digital elevation model (DEM) from contour lines is proposed. The generation of
DEM can be described as an iterative procedure, in which new contours are obtained by a weighted Euclidian distance
transformation and a consequent extraction of boundaries of the Voronoi diagram. It is characterized by the linear
interpolation with each iteration for generating contours with half contour interval of earlier ones. The performance of
the method is analyzed by both numerical tests and a topographic map test. Five mathematical surfaces are employed in
numerical tests. DEMs generated by the method proposed are comparatively evaluated with other methods, including
TLI (triangulated irregular network with linear interpolation) and TOPOGRID in ArcGIS. This algorithm gives an
effective method for producing DEM with acceptable accuracy and simple operations.
This paper presents a new method of extracting and identifying point-shaped symbols of scanned topographic maps
based on Distance Transformation. First, we remove of the interferences of transition color pixels on symbol edge, and
filter the lines which do not meet the features of point symbols according to curve density and line density, and extract
point symbols from the map based on adding hull transformation and removing skin transformation. Then we judge
topology consistency by skeleton of symbol and shape consistency by weighted distance function, and use the both
consistency to identify symbols. The algorithm can extract and identify rotated or distortional point symbols in the
condition of character and line conglutination or complex background, and the identification accuracy is 98%.
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