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 6 INTEGRATION OF IMAGE ANALYSIS AND GIS

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INVESTIGATION OF SYNERGY EFFECTS BETWEEN SATELLITE IMAGERY AND DIGITAL TOPOGRAPHIC DATABASES BY USING INTEGRATED KNOWLEDGE PROCESSING. Dietmar Kunz

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 150 this concept does not depend on other concepts, it corresponds to a disjoint object and its attributes can be fetched. The assignment of concrete attributes to a concept is called instantiation. The analysis now moves bottom-up to the concept at the next higher hierarchical level. If instances have been found for all parts of this concept, the concept itself can be instantiated. Otherwise, the analysis continues with the next concept not yet instantiated on a lower level. After an instantiation, the acquired knowledge is propagated bottom-up and top-down to impose constraints and restrict the search space. Thus, in the analysis process, top-down and bottom-up processing alternates. Generally, while performing an instantiation, it is possible to establish several correspondences between a concept and an object. However, only one of these correspondences leads to the correct interpretation. Usually, it is not possible to reliably decide at the lower levels which correspondence is correct; hence, all possible correspondences have to be taken into account. Thus, the image analysis is a search process, which can be represented graphically by a tree structure. Each node of the tree represents a state of the analysis process. If several correspondences are possible in a given state, the search tree is splitted. The analysis process continues with that node of the search tree, which is considered to be the best according to a problem dependent valuation. The problem of finding an optimal path in a search tree can be solved by the A*-algorithm. For further explanation of this tree search algorithm see Nilsson (1971), Dreyfus and Law (1977) or Kummert (1992). Its application is possible, if the path from the root node to the current node can be evaluated and if an estimation can be given for the valuation of the path from the current node to the terminal node. The functions, which evaluate the states of the analysis, are very important, since they are not only responsible for the efficiency of the search, but are also decisive for the success or failure of the analysis. The valuation of the search path is related to the valuation of the analysis goal. The valuation of the goal is calculated by considering the valuations of the instances and modified concepts, which are created in the path from the current node to the solution node. When an instantiation is performed, a hypothesis of match is implicitly established between the concept under instantiation and the chosen primitives (disjoint objects) from the database. The computed valuations for the instances and modified concepts in each state of the analysis are measures of our subjective belief in these hypotheses. They take values between 0 and 1 and can be interpreted as basic belief values in the framework of the Dempster-Shafer theory (Dempster, 1967; Dempster, 1968; Shafer, 1976; Smets, 1991). The higher a valuation, the stronger our subjective belief in the corresponding hypothesis. The different valuations are com bined and propagated in the hierarchy of the semantic network resulting thus in the valuation of the analysis goal. Two aspects are evaluated for our hypotheses of a match: the compatibility and the fidelity of the model. The compatibility evaluates an analysis state considering the principles of perceptual grouping. It is calculated based on the geometrical, topological and radiometric properties of the disjoint object primitives. The compatibility can be seen as a measure of the ability to form an object of the generic model with the chosen image primitives. The fidelity of the model determines the quality of fit between the attributes of image primitives and the statistically learned attributes from the analysis of the DLM- information. Model fidelity is a measure of the ability to form exactly that object, which is predicated by ATKIS. 3.3. Result of the Semantic Classification The result of the semantic network analysis is a new knowledge base of classified disjoint objects. Each object belongs to one of the four basic object classes but describe only a small part of the whole topographic object. Therefore, disjoint objects with the same semantic meaning and a common border are merged. The result is a complete semantic description of the scene. 4. CONCLUSIONS There is a basic need for techniques to analyse image data in an automated way. With the here presented method, it is possible to get good results without human interaction. Many refinements have to be implemented until this goal is achieved. Experiences with an extended feature base and a special segmentation process confirm the efficiency of our concept by leading to a better separability of object classes. More suitable features should be found in order to increase the knowledge base used in the semantic classification process. Until now, a comparison between the presented segmentation process and classical processes is missing. The determination of good valuation functions for spectral as well as non-spectral features in the decision process that is performed in the semantic network has been proven to be a very complex and time consuming task. The structure of the semantic net is still simple, and can be extended in an easy way by adding new concepts and links. A semantic network for the classification is one knowledge representation among many others and its potential has to be verified with additional investigations. ACKNOWLEDGEMENTS This project is funded by the DFG (German Research Foundation) II C 5 - BA 686/10-3. REFERENCES AdV: Amtliches Topographisch-Kartographisches Informationssystem (ATKIS), 1989. Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (AdV), Hannover, Germany.

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