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 Axchives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3^4 June, 1999 130 KNOWLEDGE BASED INTERPRETATION OF MULTISENSOR AND MULTITEMPORAL REMOTE SENSING IMAGES Stefan Growe Institute of Communication Theory and Signal Processing, University of Hannover Appelstrasse 9a, D-30167 Hannover, Germany WWW: http://www.tnt.uni-hannover.de/~growe E-mail: growe@tnt.uni-hannover.de KEY WORDS: Knowledge Based Image Interpretation, Semantic Net, Sensor Fusion, Multitemporal Image Analysis. ABSTRACT The increasing amount of remotely sensed imagery from multiple platforms requires efficient analysis techniques. The leading idea of the presented work is to automate the interpretation of multisensor and multitemporal remote sensing images by the use of common prior knowledge about landscape scenes. The presented system is able to use specific map knowledge of a geoinformation system (GIS), information about sensor projections and temporal changes of scene objects. The prior knowledge is represented explicitly by a semantic net. A common concept has been developed to distinguish within the knowledge base 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. In this presentation, the basic structure of the system and its use for sensor fusion on different structural and functional levels is presented. Results are shown for the extraction of roads from multisensor images. The approach for the analysis of multitemporal images is illustrated for the interpretation of an industrial fairground. KURZFASSUNG Um die immer größer werdende Menge an Femerkundungsbildem bearbeiten zu können, werden in zunehmendem Maße effiziente Aus werteverfahren benötigt. Die Kemidee der vorliegenden Arbeit ist es, die Interpretation von multisensoriellen und multitemporalen Luft bildern durch die Nutzung von Vörwissen über die Landschaftsobjekte zu automatisieren. Das vorgestellte System ist in der Lage, spezifi sches Kartenwissen eines Geoinformationssystems, Informationen über Sensorabbildungen und über zeitliche Veränderungen der Szenenobjekte für die Auswertung zu nutzen. Das Vörwissen wird explizit in einem semantischen Netz abgelegt. Es wurde ein allgemei nes Konzept entwickelt, um innerhalb der Wissensbasis zwischen Objektsemantik und visueller Abbildung in den verschiedenen Senso ren zu unterscheiden, wobei sowohl das physikalische Prinzip des Sensors als auch die Material- und Oberflächeneigenschaften der Ob jekte berücksichtigt werden. In diesem Beitrag werden die Grundstruktur des Systems und dessen Nutzung für die Sensorfusion auf verschiedenen strukturellen und funktionalen Ebenen erläutert. Beispielhaft werden Ergebnisse für Extraktion von Straßen aus multisen soriellen Bildern präsentiert. Weiterhin wird ein Ansatz für die Analyse von multitemporalen Bildern vorgestellt und am Beispiel der Interpretation eines Messegeländes illustriert. 1. INTRODUCTION The automatic extraction of objects from aerial images for map updating and environmental monitoring represents a major topic of remote sensing. However, the results of low-level image processing algorithms like edge detectors are in general incomplete, fragmented, and erroneous. To overcome these problems, a scene interpretation is performed which assigns an object semantic to the features segmented in the remote sensing image. Prior knowledge about the objects should be used to constrain the object parameters and to reduce the uncertainty of the interpretation. To increase or decrease the reliability of competing interpretations, structural relationships of the objects could be exploited. data can be accessed by computers directly and is therefore usable for the automatic interpretation of aerial images. For remote sensing, different sensors such as optical, thermal, and radar (SAR) have been developed which collect different image data of the observed scene. The wish to extract more information from the data than it is possible using a single sensor system alone raises the question of sensor fusion. Several parameters influence the data fusion: the different platform locations, the different spectral bands (optical, thermal, or microwave), the sensing geometry (e.g. perspective projection or SAR geometry), the spatial resolution, and the season at image acquisition. State-of-the-art-systems must be able to combine information from different sensors. A partial interpretation already exists for most landscapes: the map corresponding to the observed scene. Due to the growing availability of geographic information systems (GIS), the map Especially for environmental monitoring, it is necessary to investigate images from different acquisition times to study the development of the observation area. The quality of a scene

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