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Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
Ulrich Rhein
ISPA, University of Vechta, P.O. Box 1553, D-49364 Vechta, Germany, urhein@ispa.uni-vechta.de
KEYWORDS: Remote Sensing, GIS, Knowledge-Base, Change Detection, Classification, Training Data.
Environmental monitoring analysis within an integrated GIS and remote sensing environment will be a main requirement for past and
future projects. There is a continuously increasing synergy between GIS and remote sensing because there is a growing demand for
spatial information, especially of high accuracy and currency. Major developments in the analysis process will be the automated
extraction of accurate information from high resolution remote sensing imagery. New techniques, such as the use of knowledge-
based systems, hierarchical processing, artificial neural network analysis or combinations of these techniques have shown great
potential in a number of environmental monitoring studies. This paper presents the development of a robust classification technique,
which is based to a large degree on standard or operational components. Any supervised classification approach needs excellent
ground truth data to train the algorithm. It is a well-known fact that training set characteristics have a significant - if not the most
significant - effect on the performance of any image classification. Ground truth campaigns over time series, however, can be very
time consuming and expensive. The ultimate goal is the automated detection of changes in our environment. Regarding this goal, our 1
strategy is not to detect areas where changes have occurred but rather those without changes. These areas will be stored in a GIS t
database and will provide the basis for a hierarchical knowledge-based classification. This project utilises common robust e
classification algorithms to discriminate between the major landcover types. An automated GIS process algorithm (buffer, mask, c
overlay, etc.) will be used to eliminate or modify prospective training areas with multimodal histograms. The evaluation of the results '<■
in a qualitative and quantitative manner will be part of future work. c
We are modifying our environment at unprecedented rates and
scales. We can, however, debate the specific spatial dimensions,
rates and significance of these changes. Throughout history,
technology has always been a key factor facilitating change.
Current technology can create environmental changes at
previously unknown spatial and temporal scales. Yet, it also
offers us the ability to facilitate our investigations leading to a
more complete understanding of human impact on our
environment. Through appropriate use of technologies, we can
move a significant step towards an environmentally sound
management of the Earth’s natural resources (Ehlers, 1996).
Classification strategies have often been highlighted themes of
treatises. Argialas and Harlow (1990) gave an extensive
overview of various approaches for image classification and
image understanding within remote sensing. Beyond this,
automation of image classification tasks is a dream of the image
interpretation community. Various good but specific results
were provided for example by Huang and Jensen (1997). They
presented a machine-learning approach for acquiring
knowledge. Another goal for various tasks, ranging from image
acquisition to image classification, is their faster execution. The
ultimate goal of this project is an automated change detection.
The evaluation of robust monitoring analysis methods needs
comparably exact databases with information on landuse classes
and their respective changes. On the one hand, our approach has c
to deal with change detection for agricultural areas. On the I
other hand, we deal with changes of natural/agricultural areas s
near urban settlements. The latter means that the investigated i
areas may be rural/agricultural with embedded settlements.
Besides this important fact, accurate training sites and data
should be guaranteed. The main data source for our analysis are
Landsat-TM scenes. Other satellite remote sensing data are
SPOT-Pan and SPOT-XS images. For special test sites, scanned
aerial colour infrared (CIR) images are available. These aerial
images are used to support the definition of test areas and to
check the classification accuracy. Topographic maps at a scale
of 1:5000 are used as the geometric basis for mapping ground
truth areas.
In addition, digital data from the German Authoritative
Topographic Cartographic Information System (ATKIS) are
used not only for the visualisation of geocoding errors but also
for defining ground control points for the rectification process.
The use of ATKIS as a masking layer for individual classes is
possible in most cases. In this approach, however, this
procedure is not applicable for all cases. For example, farmland
and pasture areas must have a minimum size of one hectare to
be included into the ATKIS database. It is a well-known fact,
that any supervised classification needs excellent ground truth
data to begin with. It has been argued, that training set
characteristics have the most significant effect on the
performance of any image classification (Swain and Davis,
1978). Training data should be collected as close as possible to
the date of remote sensing data acquisition. Table 1 gives an
overview of our current database.