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 149 performed between objects from both scene descriptions; also intersecting objects inside one scene description are treated. The object identification codes and the classes of the original objects are stored in the new ones. The resulting new objects have now new hybrid classes, like ‘settlement from the DLM’ and ‘water from the segmentation’. An object has possibly up to 6 subclasses. Altogether 80 hybrid classes are possible. These new objects are geometrical disjoint and have an unambiguous attribute for their class. Therefore, the result after building of the disjoint objects is one new scene description, where the semantics of the produced disjoint objects are not limited to the topographic object classes 'settlement', 'forest', 'water' or 'agriculture'. There are new hybrid classes consisting of two or more topographic classes. The spectral as well as the non-spectral features are extracted for the disjoint objects. This information is then used as the knowledge base in the next step of semantic classification. 3.2. Semantic Classification Given that the problem of the geometry is treated in the previous section, only the landuse semantics of the objects are now of interest. The knowledge about the disjoint objects lies on a higher symbolic level; thus, a system for knowledge representation is used. Semantic networks are common schemes concerning knowledge representation. So far, semantic networks have been used in speech recognition (Kummert, 1992), industrial (Niemann et al., 1990a) and medical (Bunke, 1985) applications and aerial image analysis (Koch et al., 1997). The use of a semantic network for satellite image analysis is new. For using and modelling the knowledge about the disjoint objects - as well as serving as central control unit, ERNEST (Erlanger Semantisches Netzwerksystem) is applied in this research (Niemann et al., 1990b; Kummert et al., 1993). Based on the experiences with ERNEST in aerial image analysis at the IPF (Quint, 1997), a semantic net is designed for the classification process (see Fig. 14). In the semantic network system Ernest, there are three different types of links between two nodes: part-of (bst), specialization-of‘ (spez) and concrete-of (kon). The links describe the relation between two nodes. The nodes represent various objects, events, ideas, or abstract concepts. There are three different types of nodes: concept, modified concept and instance. At first, only concepts exist. During the analysis, modified concepts are distinguished from concepts only by more restricted ranges for their attribute values. When a modified concept has concrete values, it becomes an instance. A semantic network contains two different types of knowledge: declarative and procedural knowledge. Declarative knowledge consists of concepts and links, while procedural knowledge contains methods for determination of attributes of concepts, as well as for valuation of concepts and relations. The concept primitive is the interface to the database. It fetches unused and unclassified objects from the database and stores them in the concept object. The semantic net retains in the concept unused the object primitives that were not already used. Because topographic objects consist of one or more outer and inner objects, the contour is built in the concept contour. This contour object is classified because of its features to one of the semantic meanings. The process is repeated until all disjoint objects are classified. 3.2.1. Data Analysis Because the scene consists of the four area-based object classes ‘settlement’, ‘forest’, ‘water’ and ‘reject’, the learned features from Section 2 were stored in these concepts. It is assumed, that the ‘reject’ class is the same as the ’agriculture’ class, because most of the unspecified area in the DLM200 belongs to this class. This assumption simplifies the modelling of the concepts on the semantic level. In a future refining, the ‘reject’ concept could be split up in a concept for the agriculture class and a concept for objects who could not be classified with a minimum probability. In contrast to the automatically learned statistical class features, the valuation and analysis function have to be implemented by an operator. Fig. 14. Semantic net for the semantic classification. The strategy of analysis process is a general, problem independent strategy provided by the semantic network ERNEST. The analysis starts by creating a modified concept for a concept. A modified concept is a preliminary result and it reflects constraints for the concept that have been determined out of the context of the current analysis state. Following the top-down hierarchy in the semantic network, the concepts on lower hierarchical levels are each expanded stepwise until a concept on the lowest level is reached. Since

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