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:: KNOWLEDGE BASED INTERPRETATION OF MULTISENSOR AND MULTITEMPORAL REMOTE SENSING IMAGES. Stefan Growe

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 layer. Topological relations provide information about the kind and the properties of neighboured objects. Therefore, the class of attributed relations (attr-rel) is introduced. In contrast to other relations, this one has attributes which can be used to constrain the properties of the connected nodes. For example, a topological relation close-to can be generated which restricts the position of an object to its immediate neighbourhood. The initial concepts which can be extracted directly from the data are connected via the data-of link to the primitives segmented by image processing algorithms. For the efficient representation of multiple relations, the minimum and maximum number of edges can be defined in the knowledge base. The minimum quantity describes the number of obligatory relations and the difference to the maximum quantity represents the number of optional relations between objects. In this way, it can be easily modelled that for example a crossroad consists of three up to five intersecting roads. Additionally, for each edge a priority can be defined in order to realize an ordered evaluation of the relations. Edges with high priority are instantiated first. For the application of landscape analysis for example, it can be guaranteed that the streets are extracted prior to the rivers. Some relations appear exclusively in certain domains. For example roads have always a lane but they have pavements in urban areas only. This fact is taken into consideration by a domain dependent relation in the generic model. Fig. 1 shows a simple semantic net for a generic model of a Road Net which is defined as a composition of at least one Road, illustrated by the set [1,«]. A Road consists of one or two lanes. Its specialization Major Road inherits the properties of Road and possesses an additional Crash Barrier. For the part-of relation between pavement and road the domain Urban Scene is defined. Only in urban scenes this relation is valid and the system searches for pavements. All the initial objects Crash Barrier, Lane, and Pavement are represented by a Stripe-Form in the image. Figure 1. Example for a semantic net: The scene contains at least one Road. The Pavement is defined for the domain Urban Scene. The more special concept Major Road inherits the properties of Road. All objects are represented by a Stripe-Form in the image. 2.2. Control of the Scene Analysis To make use of the knowledge represented in the semantic net control knowledge is required that states how and in which order scene analysis has to proceed. The control knowledge is represented explicitly by a set of rules. The rule for instantiation for example changes the state of an instance from hypothesis to complete instance, if all subnodes, which are defined as obligatory in the concept net, have been instantiated completely. If an obligatory subnode could not be detected, the parent node becomes a missing instance. An inference engine determines the sequence of rule execution according to a given strategy. A strategy contains a set of rules out of the rule base. For each valid rule, a priority is defined to determine in which order the rules are tested. The first matching rule is fired. The user can modify the interpretation strategy by changing the priorities and by removing or inserting rules to the current strategy. The default strategy prefers a model-driven interpretation with a data-driven verification of hypotheses. Topological relations are instantiated as soon as possible to realize a spatial reasoning. Whenever ambiguous interpretations occur, for example if more than one suitable image primitive is found for a hypothesis, they are treated as competing alternatives and stored in the leaf nodes of a search tree. Each alternative is judged by comparing the measured object properties with the expected ones. The judgement calculus models imprecision by fuzzy sets and considers uncertainties by distinguishing the degrees of necessity and possibility (Dubois, 1988; Tonjes, 1999). The judgements of attributes and nodes are fused to a judgement of the whole interpretation. The best judged alternative is selected for further investigation. Starting at the root node of the concept net, the system generates model-driven hypotheses for scene objects and verifies them consecutively in the data. Expectations about scene objects are translated into expected properties of the corresponding image primitives to be extracted from the sensor data. Suitable image processing algorithms are activated and the semantic net assigns a semantic meaning to the returned primitives in a data-driven way. Interpretation stops, if a given goal concept is instantiated completely or no further rule of the current strategy can be fired. 3. KNOWLEDGE BASE FOR THE INTERPRETATION OF REMOTE SENSING IMAGERY For object extraction, only those features are relevant that can be observed by the sensor and that give a hint for the presence of the object to be extracted. Hence, the knowledge base contains only the necessary and visible object classes and properties. The network language described in chapter 2.1. is used to represent the prior knowledge by a semantic net. In Figure 2 a generic model for the interpretation of remote sensing images is shown. It is divided into the 3D scene domain and the 2D image domain. The 3D scene domain splits into the semantic layer and the physical layer. If a geoinformation system (GIS) is available and applicable, an additional GIS layer can be defined representing

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