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

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
134
Figure 3. Sensor Fusion demonstrated on the aerial view of a purification plant: Rejected (thin lines) and accepted (wide lines) road features
from (a) optical and (b) infrared image with (c) fusion result.
integration and simultaneous interpretation of images from
multiple sensors. New sensor types can be introduced by simply
defining another specialization of the Sensor node with the
corresponding geometrical and radiometrical transformations.
According to the images to be interpreted, the different sensor
layers (SAR, IR, optical) are activated.
For the application of road extraction, the advantages of a
multisensor image analysis are illustrated in Fig. 3. Using only
the aerial image (a) or the infrared image (b) yields fragmented
results. If both images are analyzed simultaneously, the gaps can
be closed. In those areas where both images provide a hint for a
road segment the reliability of the interpretation is increased. In
other areas, the information from the images complement each
other. Other examples for the fusion of multisensor images are
given in (Tonjes, 1998).
5. INTERPRETATION OF MULTITEMPORAL IMAGES
Currently, the system is being extended for the interpretation of
multitemporal images. Applications like change detection and
environmental monitoring require the analysis of images from
different acquisition times. By comparing the current image with
the latest interpretation derived from the preceding image, land
use changes and new constructions can be detected. In the
following, the necessary extensions to a multitemporal analysis
with the system AIDA are described. Preliminary results are
shown for the extraction of an industrial fairground.
5.1. Extension of the Knowledge Based System
The easiest way to generate a prediction for the current image
from an existing scene interpretation is to assume that nothing
has changed during the elapsed time. The latest scene
interpretation represented in an instantiated semantic net is
therefore regarded as a kind of GIS and it is used to guide the
analysis of the current image. The objects found in the last image
are verified in the current one but changes are difficult to detect
and to explain. However, in many cases humans have knowledge
about possible or at least probable temporal changes. Hence, the
knowledge about possible state transitions between two time
steps should be exploited in order to increase the reliability of the
scene interpretation.
Temporal changes can be formulated in a so called state
transition graph where the nodes represent the temporal states
and the edges model the state transitions. To integrate the
transition graph in a semantic net the states are represented by
concept nodes which are connected by a new relation: the
temporal relation (see Fig. 4). For each temporal relation a
priority can be defined in order to sort the possible successor
states by decreasing probability. As states can either be stable or
transient, the corresponding state transitions differ in then-
transition time which can be also specified in the temporal
relation. As normally no knowledge about the temporal changes
of geometrical objects or materials is available, the state
transition diagram is part of the semantic layer (compare Fig. 2).
In contrast to hierarchical relations like part-of or con-of, the
start and end node of temporal relations may be identical -
forming a loop - to represent that the state stays unchanged over
time. Figure 4 shows a simple example of a state transition graph
consisting of three states. For the different transitions priorities
and transition times are defined.
To exploit the temporal knowledge, a time stamp is attached to
each instance of the semantic net which documents the time of its
instantiation. Thus, it will be possible to filter time slices out of
the semantic net. The possible time stamps are given by the
Figure 4. State transition graph represented by concepts of a
semantic net. To each temporal relation a priority and a
transition time can be assigned.