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:: GEORIS : A TOOL TO OVERLAY PRECISELY DIGITAL IMAGERY. Ph.Garnesson, D.Bruckert

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 GEORIS : A TOOL TO OVERLAY PRECISELY DIGITAL IMAGERY Ph.Gamesson 1 , D.Bruckert 2 1 ACRI, 260 route du Pin Montard, 06904 Sophia Antipolis, France, pg@acri.fr 2 WEUSC, Aparto de Correos 511, 28850 Torrejon de Ardoz - Madrid, Spain, bru@weusc.es KEYWORDS: Satellite Images, Combination, Feature Matching, Precise Geo-Location. ABSTRACT The main requirement for satellite and airborne imagery exploitation is to make conform (overlay) these images (optical, radar, infrared, etc) to a working geographic model that can be either a map or another reference image. The GEORIS tool method is to accurately overlay an image with any other image by using only the image contents. The image analyst community dealing with operational crisis management and fast decision making has required this important operational constraint. Indeed, in operational near-real-time context, different images and maps shall be often combined without knowledge of the sensor characteristics. To solve the current polynomial-registration-method drawbacks, a new state-of-the-art technique has been designed and developed using a registration model which integrates ground control and tie points, as well as recognised linear features (roads, power lines, railways, pipe and water networks, bridges, buildings, shorelines, etc). Firstly, linear features are used to calculate the polynomial model accuracy and estimate its residual quality. Secondly, the linear features are used to define a graph model including thousands of control points. Then, a numerical process to match the graphs is used to optimally estimate the best registration model. For mountainous areas or inaccurate maps, a polynomial model cannot be applied, even if it has a high order. The developed GEORIS method proposes a multiple local fitting model approach. Such a technique has the advantage of taking into account local relief and increases significantly the matching precision. To improve further the relative pixel positioning accuracy, the last GEORIS version is able to use a digital elevation model when available. 1. INTRODUCTION To exploit a set of digital spacebome and airborne images and maps either for image analyses and data fusion or more sophisticated data post-processing like with GIS facilities, it is necessary to register all of them in a common geographic co ordinate system. From an operational point of view, the obtained registration accuracy conditions the reliability of the derived information, extracted from the data. Therefore, this step is important and requires careful methodology to perform the multiple source data registration (spatial and spectral) and its quality control. The quality control of the registration accuracy provides to the user confidence that the set of images and maps can be correctly matched to extract reliable information. Classically, geometric registration is based on two different approaches. The first approach, is based on the use of sensor geometric models, including observation features and orbital parameters (Crawford et al., 1996, de Sève et al., 1995, El- Manadili and Novak, 1996). In this case, requirements are the availability of complementary information such as satellite ephemeris, correct sensor models and sometimes digital elevation models which are usually not supplied with the set of images (Palà and Pons, 1995). In addition, in a real-time operation context, such as for natural disasters (floods, earthquakes, fires, etc), these required additional data are most of the time not immediately available or even not existing. The second, more empirical, approach relies on matching ground control points (Jensen, 1996). The drawback is the difficulty to automate the process of registration which is done under the control of an operator, except in some cases where a database of reference GCPs has been pre-computed (Holm et al„ 1997). In this study, the second approach has been investigated. To satisfy operational needs, the provided images shall be exploited as they are. In other terms, only spacebome and airborne image contents are used and contribute with other data sources to characterise the risk and its temporal development. Therefore, to support such operational requirements, a procedure and methodology shall be designed to integrate images, maps and site reports as they are and produce an accurate overlay of all this data. In this context, a system called GEORIS has been designed and developed by the ACRI company under a WEUSC contract (Western European Union Satellite Centre, Madrid). The aim of this article is to provide an overview of the GEORIS system, and especially of the model used. 2. GEORIS MODEL The core of the software computes mathematical models to geometrically register an image with a second image or map which is used as the reference (the reference image is usually a geo-coded image called master).

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