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 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.

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