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PRIMARY DATA ANALYSIS AND PREPARATION FOR DTM GENERATION
G. Aumann, K. Eder, A. Pfannenstein, R. Würländer
Chair for Photogrammetry and Remote Sensing
Technical University Munich
Arcisstr. 21, D-8000 Munich 2, Germany
Tel: + 49-89-2105 2671; Fax: + 49-89-280 95 73; Telex: 522854 tumue d
E-mail: anton@photo.verm.tu-muenchen.de
Commission IV
ABSTRACT:
Experience in digital terrain modelling has shown, that
analysis and preparation of the primary data is of great
importance both for the productivity and the quality of
DTM modelling.
The paper presents a semi-automatic procedure to
analyze the primary data in a numerical and graphical
way. After gross error detection and data structuring
using a triangular irregular network (TIN) the density
and distribution of the data is checked with the aim to
get adequate parameters for the final DTM generation
by the Finite Element Method.
In addition to this, tools for data completion and data
refinement (e.g. automatic derivation of skeleton lines)
can be supplied. The effect of the data preparation can
be visualized quickly by updating the TIN and derived
follow up products.
Key Words: Data Quality, DTM, Preprocessing.
1. INTRODUCTION
During the last decade digital terrain modelling
technique has reached a rather high standard. Efficient
program packages are available with sophisticated
approaches for DTM interpolation and utilities for the
derivation of various "follow-up" products (Ebner et.al.,
1988, Kóstli et.al., 1986).
An important component, however, the primary data
analysis and preparation, is not yet solved to the benefit
of practical use. Therefore a concept has been devel-
oped which supplies tools for gross error detection and
quality control of the primary data, as well as for data
850
refinement and completion. The aim is to set up an
optimal data set and to offer default values for the final
DTM generation with the finite element method.
2. DTM PRIMARY DATA AND THEIR
CHARACTERISTICS
According to the methods of data acquisition applied, a
variety of input data sources and types has to be
considered.
Three groups of data sources can be distinguished:
photogrammetric data
Photogrammetric data acquisition is very common for
medium and small scale DTM-projects. Data types are
regular grids or equidistant profiles but also arbitrarily
distributed reference points may be supplied. A pe-
culiarity of photogrammetric data acquisition is the
measurement of a variable grid (progressive sampling)
where an initial grid is densified semi-automatically
according to the terrains undulation (Markarovic, 1973).
Geomorphological information is given in form of break
lines, skeleton lines (ridge and valley lines) and specific
points (hilltops, hollows, saddle points). One of the main
advantages of photogrammetric data acquisition is, that
the geometric and geomorphological quality of the data
can be checked by on-line verification with the stereo
model (Reinhardt, 1991). Recently automatic proce-
dures have been developed based on digital image corre-
lation algorithms (Heipke, 1990). These approaches
supply very dense point distributions but there may be
areas without reference point coverage where no corre-
lation was possible.
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