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 BAYESIAN METHODS: APPLICATIONS IN INFORMATION AGGREGATION AND IMAGE DATA MINING Mihai Datcu 1 and Klaus Seidel 2 German Aerospace Center (DLR), German Remote Sensing Data Center (DFD) D-82234 Oberpfaffenhofen, email: Mihai.Datcu@dlr.de 2 Image Science Division, Communication Technology Laboratory, ETHZ CH-8092 Zürich, email: Klaus.Seidel@vision.ee.ethz.ch KEYWORDS: Remote Sensing, Data Fusion, Information Mining, Bayesian Methods. ABSTRACT More accurate interpretation of remotely sensed data is based on a concept combining synergistically signals, information or knowledge from different sources. The aim is information mining, extraction and presentation. A hierarchical structure of data fusion levels has been identified: on image signal level, on image features, on physical parameters extracted from images, on meta features resulting from image feature modelling, on feature grouping. The Bayesian perspective is discussed aiming at a variety of aspects. The power of the Bayesian approach is endowed i. e. with the possibility to analyse uniformly the uncertainties over scene parameters in data acquired from heterogeneous and incommensurable sources. 1. PRELIMINARIES The Earth coverage has high complexity, thus making difficult their recognition and characterization using observations provided by a single sensor. More accurate interpretation of remotely sensed scenes is obtained by synergistically combining signals, information or knowledge from different sources. The data fusion at signal level shows limited success for the automatic interpretation of remote sensing data. The reason is the incommensurability of the information in its raw format, the image samples. However, it shows promising results as a visualization technique, e.g. the presentation of a scene in a computer graphics environment, using synthetic aperture radar, interferometrically derived terrain models and surface texture derived from optical images. Dealing with heterogeneous sources of information is of key importance for the design of appropriate information representation and its levels of abstraction. Thus, data fusion is replaced by information fusion. Methods showing promising results are based on the knowledge of the physical model of the candidate scene. In situations of poor knowledge of the scene and image formation models, fuzzy methods and evidential reasoning or other vague information representations have been successfully applied. In situations when the models are known, the Bayesian inference allows a precise description of the problem, and thus, accurate scene information retrieval. The power of the Bayesian approach consists in the possibility to represent uniformly the incertitude over a certain scene parameter in data acquired from heterogeneous and incommensurable sources. The incertitude has the form of the posterior distribution of the desired scene parameter, given the observations from several sources. The posterior distribution is inferred from the likelihood of each data source and the a priori model of the parameter. The likelihood distribution takes into consideration the forward model of the specific sensor and its noise. The a priori information models describe the behaviour of the desired parameter under the observation conditions, which are specific for each sensor type. As an example, the accuracy of the classification of the landcover from synthetic aperture radar can be improved using information from optical images. The posterior distribution of the landcover parameters is expressed as being conditioned by both the synthetic aperture radar and optical data. 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. 2. THE BAYESIAN INFERENCE AND HIERARCHIC DATA MODELLING The Bayesian approach consists of interpreting probabilities based on a system of axioms describing the incomplete information, rather than randomness. Hence, Bayesian approaches are very well suited for the extraction of information out of observed data. Using Bayesian techniques, one can extract the most probable scene parameters explaining the observed data under the assumption of some prior knowledge. The prior knowledge is expressed in form of stochastic models. The Bayesian methods allow also to choose the most plausible model from a given class. Two levels of inference are introduced (MacKay, 1991): Model fitting. The first level of inference assumes that the used models are true. The task is to fit the assumed model to the data D, estimating the most plausible model parameter values 0 and

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