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 5 FUSION OF VARIABLE SPATIAL / SPECTRAL RESOLUTION IMAGES

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: OPERATIONAL APPLICATIONS OF MULTI-SENSOR IMAGE FUSION. C. Pohl, H. Touron

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 123 OPERATIONAL APPLICATIONS OF MULTI-SENSOR IMAGE FUSION C. Pohl ’, H. Touron 2 1 International Institute for Aerospace Survey and Earth Sciences (ITC), P.O. Box 6, 7500 AA Enschede, The Netherlands, pohl@itc.nl 2 Western European Union Satellite Centre (WEUSC), P.O. Box 511, 28850 Torrejon de Ardoz (Madrid), Spain KEYWORDS: WEU, Visual Interpretation, Data and Image Fusion, Operationalization, Complementarity ABSTRACT Space-based observation provides repeated, unrestricted access to every comer of the globe, in full compliance with international law, and this capability may provide early warning of crises before action has to be taken to deal with them. Risks can be assessed before they turn into threats. The Western European Union Satellite Centre (WEUSC) operationally exploits imagery derived from different Earth observation satellites for security and defence purposes. At the WEUSC, the remote sensing data is digitally processed to enhance visual image interpretation capabilities. One of the processes applied is image fusion. This paper reports on the experiences gained using image fusion as a tool to integrate multi-sensor images in order to benefit from increased spatial, spectral and temporal resolution, in addition to increased reliability and reduced ambiguity. After a short introduction, the concept of image fusion as it is implemented at the WEUSC is described, followed by an explanation of the processing involved. An overview of operational applications using image fusion as a major step prior to visual image interpretation allows the compilation of a list of operational statements, vital in the implementation of image fusion. A very important factor of applying image fusion is the integration of complementary data. The complementarity of visible and infrared (VIR) with synthetic aperture radar (SAR) images is a well known example, where the objects contained in the images are 'seen' from very different perspectives (wavelength and viewing geometry). The integration of high resolution and multispectral information forms another type of complementarity. This paper provides an overview of issues in operationally-used image fusion relating to the processing involved and discusses benefits and limitations of approaches, illustrated by examples. All results have to be viewed in the context of visual image exploitation. 1. INTRODUCTION Multi-sensor data fusion appears to be widely recognized in the remote sensing user community. This is obvious from the amount of conferences and workshops focussing on data fusion, as well as the special issues of scientific journals dedicated to the topic. Previously, data fusion, and in particular image fusion belonged to the world of research and development. In the meantime it has become a valuable technique for data enhancement in many applications. More and more data providers envisage the marketing of fused products. Software vendors started to offer pre-defined fusion methods within their generic image processing packages. The WEUSC operationally exploits images from many different sensors available on the market in order to respond to requests from the WEU council or WEU Member States in the fields of general security surveillance, support for Petersberg missions 1 and surveillance in more specific spheres. In order to perform multi-sensor image interpretation as required operationally from the Satellite Centre, the image analysts process the imagery to obtain enhanced and suitable image products. One of the approaches utilized is image fusion. Tools for and training material on image fusion have been developed and implemented 1 Petersberg missions: WEU term to describe missions ranging from humanitarian and rescue tasks to tasks involving combat forces in crisis management, including peacemaking. in order to support the daily work of the image analysts. The data fusion system (DFS) is used to benefit from improved spectral, spatial and temporal resolution. Furthermore, the consideration of multi-sensor data ensures the availability of data when it is needed and the replacement of deficiencies contained in satellite imagery (e.g. cloud cover). In addition, image fusion can contribute to more reliability and reduced ambiguity of the interpretation results. The following sections provide a short description of the fusion approach established and experiences obtained in an operational environment illustrated by real world examples. 2. IMAGE FUSION Image fusion aims at the integration of complementary data to enhance the information content of the imagery, i.e. make the imagery more useful to a particular application. From the experiences gained in the past, it is clear that the selection of an image fusion approach depends on the desired application. The definition of image combinations and techniques depends on the characteristics a dataset should have in order to serve the user (Pohl and van Genderen, 1998). However, it is possible to summarize a general approach, which describes the overall processing chain needed in order to achieve image fusion (see Fig. 1.). Basically, the dataset to be fused has to be pre- processed in order to achieve conformity or data alignment as defined by Wald (1998b). In the case of multi-sensor image

Access restriction

Copyright

fullscreen: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation