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 131 interpretation can be increased by using the information from preceding images. Hence, it becomes possible to distinguish for example between the construction and the dismantling of buildings or between the regeneration and degeneration of moorland areas [Pakzad, 1999]. To realize such a multi temporal analysis, an interpretation system must be able to administrate images from different time instances and to represent and exploit information about possible or at least probable temporal changes. The leading idea of this work is to automate the evaluation of aerial images of complex scenes using prior knowledge about the object structure, GIS, sensor type, and temporal changes. To ease the adaptation of the analysis system to new requirements and the extension to future tasks, the knowledge is represented explicitly and is separated from system control. Such a so-called knowledge based approach constitutes the focal point of this work. In the literature various approaches to image interpretation and sensor fusion have been presented. Only a few authors try to formalize the representation of the objects and sensors, and the control of the information integration. Most interpretation systems like SPAM (McKeown, 1985) and SIGMA (Matsuyama, 1990) use a hierarchic control and construct the objects incrementally using multiple levels of detail. The system MESSIE (Clement, 1993) models the objects explicitly distinguishing four views: geometry, radiometry, spatial context, and functionality. It employs frames and production rules. In the BPI system (Stilla, 1997) a net of production rules representing a part-of-hierarchy describes the structural prior knowledge. A blackboard realized by an associative memory is used for process communication. Another blackboard-based architecture is suggested by Mees (1998). He distinguishes between strategy knowledge represented by an AND/OR-tree, global knowledge described by sensor-independent fuzzy production rules, and sensor-dependent local knowledge stored in attributed prototypes and image processing operators called local detectors. ERNEST (Kummert, 1993) uses semantic nets to exploit the object structure for interpretation. The MOSES system extends the ERNEST approach to extract man-made objects from aerial images (Quint, 1997). The presented system AIDA (Liedtke, 1997) adopts the idea to formulate prior knowledge about the scene objects with semantic nets. In addition, the control knowledge is represented explicitly by rules which are selected by an inference engine. In the following, the system architecture of AIDA is described and a common concept is presented to distinguish between the semantics of objects and their visual appearance in the different sensors considering the physical principle of the sensor and the material and surface properties of the objects. The necessary extensions to provide a multitemporal image analysis are described and illustrated in chapter 5. 2. KNOWLEDGE BASED INTERPRETATION SYSTEM For the automatic interpretation of remote sensing images, the knowledge based system AIDA (Liedtke, 1997; Tonjes, 1999) has been developed. The prior knowledge about the objects to be extracted is represented explicitly in a knowledge base. Additional domain specific knowledge like GIS data can be used to strengthen the interpretation process. From the prior knowledge, hypotheses about the appearance of the scene objects are generated which are verified in the sensor data. An image processing module extracts features that meet the constraints given by the expectations. It returns the found primitives - like line segments - to the interpretation module which assigns a semantic meaning to them, e.g. road or river. The system finally generates a symbolic description of the observed scene. In the following, the knowledge representation and the control scheme of AIDA is described. 2.1. Knowledge Representation The knowledge representation is based on semantic nets. Semantic nets are directed acyclic graphs and they consist of nodes and edges in between. The nodes represent the objects expected in the scene, while the edges or links of the semantic net form the relations between these objects. Attributes define the properties of nodes and edges. The nodes of the semantic net model the objects of the scene and their representation in the image. Two classes of nodes are distinguished: the concepts are generic models of the object and the instances are realizations of their corresponding concepts in the observed scene. Thus, the knowledge base which is defined prior to the image analysis is built out of concepts. During interpretation a symbolic scene description is generated consisting of instances. The object properties are described by attributes attached to the nodes. They have a value measured in the data and a range describing the expected attribute value. During instantiation the attribute range of the instance is taken from the corresponding concept and - if possible - is restricted further by the information of instantiated parent nodes. For example, an already detected street segment can constrain the position of the adjacent segment. For both attribute value and attribute range a computation method can be defined. A judgement function computes the compatibility of the measured value with the expected range. The relations between the objects are described by edges or links forming the semantic net. The specialization of objects is described by the is-a relation introducing the concept of inheritance. Along the is-a link, all attributes, edges and functions are inherited to the more special node which can be overwritten locally. Objects are composed of parts represented by the part-of link. Thus, the detection of an object can be reduced to the detection of its parts. The transformation of an abstract description into its more concrete representation in the data is modelled by the concrete-of relation, abbreviated con-of. This relation allows to structure the knowledge in different conceptual layers like for example a scene layer and a sensor

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