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 1 OVERVIEW OF IMAGE / DATA / INFORMATION FUSION AND INTEGRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INTEGRATION OF IMAGE ANALYSIS AND GIS. Emmanuel Baltsavias, Michael Hahn,

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogramme try and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 same object (implying also same date, if the object or its properties vary in time) are usually needed, map updating and change detection applications require the opposite. Note that it is not always necessary to perform the respective co-registration operations e.g. to relate two images to each other, they do not have to be geocoded, but the mathematical relations to transform from one to the other should be known. Same abstraction level. If this prerequisite is not fulfilled, a direct comparison becomes impossible, e.g. when comparing road centerlines with road information in images. Clear object definitions. This seems trivial, but in practice it is not, as it depends on definitions made by the data producer or user, and it may vary depending on application and country. As an example, terrain information on bridges is considered to be part of the DTM in Germany but in Switzerland not. Another example from a project at ETH Zurich to update the road network of the 1:25,000 maps is the definition of road centerlines. This is not necessarily the middle line strip on a two-direction road, it may include tram lines and dedicated bicycle corridors or not, while a widening of the road before intersections by additional lanes to turn right and left should generally not be included in the definition of the road width but in some cases might be needed. Need of metadata and quality indicators. Information on the data itself and how they were generated are clearly needed. Unfortunately, often data are delivered without this information which is small in size but high in importance. This has to do among other with weaknesses in data storage, management and transfer, and lack of interoperability among various systems. Quality indicators are the only way to decide on how to combine and weigh different components. This information is often not provided, or only very global measures, e.g. for the DTM of a whole map sheet, a single RMS error is provided, and for a classification map, an accuracy percentage for all classes or maybe each individual class for the whole area. For a successful integration accuracy indicators for each data unit is needed, e.g. each node of a DTM, or each class object (or even better each pixel) of a classification map. In addition, appropriate theories and tools for the interpretation, evaluation and fusion of multiple partial results are needed. Regarding the above prerequisites, some remarks will be made: • The completeness and accuracy of the data to be combined will almost always differ. Generally, GIS data are expected to be more abstract. • The differences between the data should be minimised right from the beginning. As an example, road intersections are often used as GCPs with airborne and spacebome imagery. Instead of using vector information about road centerlines to detect them in the images, image chips of such intersections coming from similar imagery could be easier detected and localised in the images to be processed. • Deep knowledge is needed about advantages and disadvantages of available data, in order to select the appropriate one, for a given application. 5. KNOWLEDGE-BASED IMAGE ANALYSIS COMPONENTS AND ARCHITECTURE We assume that in general a 3D description of a scene (site) is aimed at. The scene consists of objects. Each object has characteristics, properties, features, attributes (all these four words are treated here as synonyms). The term structure has been used to denote combinations of features (used now in the sense of object components) or of objects, e.g. the combination of edge segments might lead to the structure "closed contour", or the combination of buildings to the structure "block". The attributes of the objects can be very variable: geometric, spectral, textural, material, physical, chemical, biological, functional, temporal etc. To describe the scene raw (or derived) measurements are used. These measurements have a reference system (pixels, grid cells etc.) and provide information about some limited properties of the scene, either explicitly or implicitly, e.g. the high areal concentration of lights in night- satellite imagery may be an indication of urban areas. Furthermore, relations between objects and features exist (topology, context) which should be modelled and appropriately exploited. A priori information can exist in the form of rules (very soft to very strict), and models (e.g. roof models) or other knowledge. This a priori information encodes assumptions, constraints etc. and may relate to features or objects or the whole scene. Models, and their associated assumptions and range of validity, are needed in various other aspects, e.g. sensor models, image and noise models, terrain models, atmospheric, illumination and reflectance models etc. Finally, important components of such an image analysis system are the knowledge modelling and representation, the system architecture and control (hierarchical, e.g. top-down or bottom- up, heterarchical, e.g. blackboard architecture) and the strategy to solve a given problem. Critical questions, which should be answered by the above components, are: • Which data, knowledge and processing units should be combined, when and how? • How should the processing flow be? • How are the partial results combined? • How much human interaction is needed and when? 5.1. Knowledge, Modelling and Representation for Data Fusion There are various theories and approaches for knowledge, modelling and representation in image analysis and different system architectures. A good overview, although a bit old, is given by Abidi and Gonzalez (1992). Some of the major approaches include: • Bayesian approaches (Miltonberger et al., 1988; Quint and Landes, 1996) • Mathematical approaches: least squares, Kalman filtering, robust estimation, régularisation • Dempster-Shafer / belief (evidence) theory (Dempster, 1968; Shafer, 1976) • Frames (Hanson and Riseman, 1978) • Ruled-based systems (McKeown et al., 1985; McKeown and Harvey, 1987)

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