Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 3 OBJECT AND IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: BAYESIAN METHODS: APPLICATIONS IN INFORMATION AGGREGATION AND IMAGE DATA MINING. Mihai Datcu and Klaus Seidel

Document type:: Monograph

Structure type:: Chapter

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 structure. 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 4. THE BASIC CONCEPTS 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). 5. LEVELS OF ABSTRACTION OF INFORMATION REPRESENTATION 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 defined. • 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

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