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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
the approximation and the detail, and introduces a pyramidal data
representation similar to a quadtree. The accurate modelling of
wavelet coefficients requires the representation of both intra
scale and interscale dependencies. The intra-scale model
captures the local structural behaviour of the image. The
information within a neighbourhood in the original image is
distributed in each “quad” (brothers) and at the corresponding
coordinates of the different orientations in the detail space. The
interscale relationships are modelled as Markov chains. A more
general formalism is the multiscale stochastic modelling. In the
graph below a random field is defined having as support a tree
The interscale causalities are described by transition probability
density functions P(*l*). X is the predicted value at scale k-1 and
w an interscale error process.
These models allow synthesis of finer scales X k of a random field
beginning with its coarser scales X k 'V The stochastic process
represents the new information, in a way similar to the detail
signal in the wavelet transform domain. The models are inspired
from the multigrid techniques in numerical analysis and define
consistent Multiscale Markov Random Fields starting from a
single resolution. They can model a variety of image
configurations and are implicitly the basis for picture parameter
estimation. The estimation principle consists of solving a
sequence of global optimization problems defined on a sequence
of embedded configuration subspaces accepting constraints in
form of prior distributions (Baldwin et al., 1998; Bouman and
Liu, 1991; Luettgen et al., 1993). 4
Due to the incommensurability of the images obtained from
different sensors and due to the high complexity of the imaged
scenes, data fusion systems demand high level representation of
information. Thus, scene interpretation is done by augmentation
of the data with meaning (Jumarie, 1991). Based on the Bayesian
principles of inference and on data fission and fusion paradigms,
we present in Fig. 4 the simplified architecture of a scene
interpretation system using multiple data sources.
The information source is assumed to be a collection of
multidimensional signals, e.g. airborne or satellite images,
acquired from optical or SAR sensors. Due to the
incommensurability of the data provided by heterogeneous
sources, the information fusion process is splitted up in two steps:
i) information fission and ii) information aggregation.
Information fission is an analysing step enabling to deal with
heterogeneous sources of data, and also to cope with different
and incommensurable types of information extracted from
individual sources. The information source is partitioned in
elementary sources.
The information fission requires the signal modelling as a
realization of a stochastic process. A library of stochastic and
deterministic models is used to infer the signal model. The
information extraction is a model fitting task. The information
content of datasets acquired by individual sensors is extracted.
The resulting objective features are aggregated according to the
user conjecture. Thus, the information fusion process relies on
restructuring (using a certain syntax) the signal feature space
according to the user semantic models. At this step, the
information from incommensurable sources is ordered and
augmented with meaning, thus providing a scene interpretation
(Jumarie, 1991; Lauritzen and Spiegelhalter, 1988).
In the case of modelling high complexity signals, e.g. collections
of multi-sensor images, a large number of sources coexists within
the same system. The solution of information and knowledge
fusion as stated in Eq. 6 becomes a very difficult task. However,
in many practical applications the candidate models are likely to
be analysed hierarchically. Thus, it is desirable to integrate
probabilistic models, i.e. they should store common parts for
efficiency of the model representation, and they must be
represented hierarchically in order to capture the class structure
and to provide computational advantages. Starting from the
remotely sensed images of a scene, a hierarchy of information is
• Image data: the information is contained in the pixel
intensities of the raw data. It is the lowest level of
information representation. However, there are useful
applications, e.g. image classification based on image
intensity. Data fusion at this level of representation is limited
by the incommensurably of the measurements. Typical
applications are fusion of multiresolution data of the same
type of sensor.
• Image features: the information is extracted in form of
parameters characterising the interactions among spatially
distributed pixels, or different spectral channels.
Additionally to the parameter values, characterization of
parameters incertitudes can be obtained. Popular examples
of image features are: texture, multispectral features or
geometrical descriptors. Data fusion at this level is possible
in the case of parameters representing the same quantity.
E.g. enhance geometric precision of an edge using
information from multiple sensors.
• Physical parameters: the image features reflect the physical
parameters of the imaged scene. Thus, assuming the
availability of certain models, the scene parameters can be
extracted. For example, image texture carries information
about the size of tree crowns, or the SAR backscatter of
ocean surface contains the wind speed information. Data
acquisition using different type of sensors can be fused to
increase the estimation accuracy of physical parameters, or
to complement missing observations.
• Meta features: estimation of both image features, and
physical parameters requires the assumption of some data
models. The type of model used, its evidence and
complexity, plays the role of meta information, i.e.
describing the quality of the extracted parameters. From a
data aggregation perspective, a meta feature is an indicator