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 2 PREREQUISITES FOR FUSION / INTEGRATION: IMAGE TO IMAGE / MAP REGISTRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: GEOCODING AND COREGISTRATION OF MULTISENSOR AND MULTITEMPORAL REMOTE SENSING IMAGES. Hannes Raggam, Mathias Schardt and Heinz Gallaun

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 GEOCODING AND COREGISTRATION OF MULTISENSOR AND MULTITEMPORAL REMOTE SENSING IMAGES Hannes Raggam, Mathias Schardt and Heinz Gallaun Institute of Digital Image Processing, Joanneum Research, Wastiangasse 6, A-8010 Graz, Austria {hannes.raggam} {mathias. schardt} {heinz.gallaun} @joanneum.ac. at KEYWORDS: Geocoding, Parametric Imaging Models, Coregistration, Image Matching, Polynomial Rectification. ABSTRACT The analysis of multitemporal/multisensor remote sensing datasets can only be efficiently done if the data refer to a common geometry. Geocoding of the individual images of such a dataset to the geometry of a topographic map is the most usual procedure to accomplish data comparability. Registration of the images will have to meet extremely strict requirements, if data acquired at different dates and/or with different systems will be processed simultaneously in one "data stack". The approaches to precisely coregister data from a geometric point of view can be based on (a) the utilisation of sensor specific parametric imaging models to precisely relate image pixels to ground, or (b) the utilisation of general coregistration techniques, which are independent of any sensor-specific imaging parameters. In general, parametric approaches are increasingly used for geocoding. The accuracy achieved with such methods lies in the order of one pixel. However, the experience from various applications, e.g. change detection analysis, showed the necessity of achieving mean geometric accuracies of less than half a pixel, if pixel-by-pixel comparison is to be performed. This is particularly true for classes, which are characterised by heterogeneous spatial distribution such as mixed forests, small patterns of agricultural areas or settlements. This required accuracy can be obtained under certain conditions by automated image matching and coregistration of the images with acceptable efficiency. The paper will discuss the performance of both parametric methods and matching-based coregistration techniques with regard to monitoring applications that are currently developed at the Institute of Digital Image Processing, Joanneum Research. 1. INTRODUCTION The integration of multitemporal and multisensor remote sensing data and other relevant data layers demand appropriate data coregistration methods. Only if such methods are available, the new information resulting from the combination of the source datasets can be optimally utilised by the various users. By applying data coregistration methods, new information can be gained from the complementary information content of the multiple data sources. According to various authors, data fusion can be done at different levels. Pohl and van Genderen (1998) distinguish between data fusion at pixel, feature or decision level. Csatho and Schenk (1998) use a slightly different classification, considering fusion at signal, pixel, feature and symbol level. In either case, it is recognised that the data have to undergo a respective pre-processing, including geometric but also radiometric procedures. These pre-processing steps are in general a very critical point for operational data fusion applications and, therefore, can not be seen independently from the applied data fusion methods. This paper is related to the geometric pre-processing of multitemporal and multisensor images, so that a subsequent pixel-level image fusion is possible. This geometric pre processing comprises registration or geocoding of the images, such that their corresponding pixels have the same geometry, i.e. the aim is to generate precisely coregistered datasets (see Figure 1). In this paper, the following approaches to generate coregistered images in either image or map 1 geometry will be generally discussed: • Polynomial rectification • Parametric geocoding • Matching-Based Coregistration (MBC) The discussion on MBC will focus on approaches as implemented and used at the Institute of Digital Image Processing. In this context, polynomial rectification methods are 1 Here, map stands for an object space reference coordinate system.

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