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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
72
Syntactic
Semantic
models
models
User conjecture modelling
Information fission
Information aggregation
Fig. 4. Simplified diagram of an information fusion system. Heterogenous sources of information are splitted into
elementary sources: the information fission. The information aggregation completes the fusion process.
of information commensurability, e.g. estimated texture
features using co-occurence matrix are not comparable with
parameters of Markov random fields.
• Cluster model: image features or estimated physical
parameters have n-dimensional representations. Due to
observation noise or model approximations, the feature
space is not occupied homogeneously. Thus, another level of
information abstraction, and fusion at the same time, is the
type of feature grouping, i.e. the cluster models, and the
associated parameters. Clustering can represent information
only for one category of the features.
• Syntactic modelling: stochastic grammars are defined to
represent the order of joint spaces, image features and
models. The induced syntax can be interpreted as a
clustering of incommensurable information representations.
Thus, information in incommensurable representations can
be aggregated. In the case of fusion of physical parameters
extracted from different data types, the result of information
fusion is equivalent to a supervised classification, otherwise,
for the case of abstract features, the classification is
unsupervised.
• Semantic representation: due to the impossibility to
estimate the physical parameters, an unsupervised
classification of the data is produced. Augmentation with
meaning requires a higher level of abstraction. Prior
information in form of training datasets or expert knowledge
is used to create semantic networks. Thus, the observations
are labelled and the contextual meaning is defined.
The performance of information extraction depends critically on
the descriptive or predictive accuracy of the probabilistic model
employed. Accurate modelling typically requires high
dimensional and multi-scale modelling. For heterogeneous
sources, accuracy also depends on adaptation to local
characteristics.
6. DATA AND INFORMATION MINING
One of the difficulties in many data fusion applications for
remote sensing image classification is the identification of the
datasets relevant for the user. Here, the information retrieval task
becomes an information mining task. The Bayesian inference can
be applied again, but the goal is to find the set of images,
generally acquired from different sensors, which might contain
the information desired by the user. Thus, the inference is used to
find the posterior distribution over the set of available images
according to the user hypothesis in a given application.
In the present context, data mining will be understood as
searching for datasets with certain properties, and information
mining refers to the search of hidden relationships among image
features, either in one or in multiple image datasets. Data mining
will produce an index, information mining a label.
The concept and hierarchy of information representation allows
the integration of different tools for information mining. The goal
is to explore the information content of the images and decide
which ones are relevant for the user’s application. The tool we
propose is mainly based on interactive decision using data
visualization, e.g. image browsing integrated with image
segmentation, or information visualization, e.g. exploration of
the clusters’ structure in the feature space and the interactive
design of the decision surfaces. The block diagram of a simple
data and information mining tool is presented in Fig. 5.
Recently, solutions of the MAP estimator have been proposed
combined with an expectation-maximization and deterministic
annealing, in which entropy minimization maximizes the amount
of evidence supporting each parameter, while minimizing
uncertainty in the sufficient statistics and cross-entropy between
the model and the data. A similar approach was developed using
two levels of Bayesian inference, model fitting and model
selection, for the estimation of the hidden parameters of a 3-level
hierarchical model. These methods of inference can be integrated
in tools for information mining in the assumption of datasets
explained by multiple models that coexist together.