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 with an incidence angle of 27 degrees). The second corresponds to the case of a reference map (Michelin Map) and a historic map (Cassini map of 1756). The local approach guaranties a residual value of 0 for the tie points used by the process. Table 1 shows the errors (mean, minimum and maximum values) estimated using control tie points. A considerable improvement through the use of the local correction is noted due to the fact that the local deformation is usually smooth. Polynomial approach Polynomial + local approach Mountainous area (34 control tie points) 19.5,0.5,46.6 9.1,0.7,28.3 Cassini map (22 control tie points) 9.1,4.8 , 19.9 6.2,1.1 , 13.1 Table 1. Coregistration errors (mean, minimum, maximum) for two methods (in pixels). When a DEM is available (only required for the master image), we assume that the correction to be applied is a function of the height (see Eq. 4). dxi = a Zj + b (4) dy ; = a’Zj + b’ The coefficients in Eq. 4 are estimated using the set of tie points (weighted in the same way as previously). When a DEM is available, this approach can provide more accurate results. For instance, in the case of the mountainous area, the error decreases from 9.1 to 1.3 pixels (see Table 2). Polynomial + local approach with DEM Mountainous area (34 control tie points) 1.3,0.2,2.6 Table 2. Coregistration errors (mean, minimum, maximum) when using local corractions and a DEM (in pixels). 2.3. Use of Linear Features Another important characteristic of the GEORIS software is the capability to use, in addition to the tie points, selected linear features (road, railway, power line, shoreline, pipe, etc.) to compute the global and local models. These features are directly pointed by the operator (depending on the spatial resolution and the positioning accuracy to be reached, the features should be carefully pointed at, e.g. either the road strip or one of the edges of the road or the centerline of the road). When pointing a linear feature (i.e. road strip), it is not required that the beginning and the end of the linear feature have the same location in both images to be registered; only a common segment is needed. The linear features can be used either to calculate the registration error (from the results of matching of the linear features after the global and local models are applied to the image to be registered) or to compute the models themselves by using thousand of tie points which are automatically generated along the selected linear features. The registration error is estimated using a statistical approach. For each point of a considered feature, the model is applied and we search the nearest pixel of the corresponding feature in the other image. The distance obtained is used as an estimate of the matching accuracy. To limit matching errors, the matched pixels which involve the end points of the feature are ignored. This very simple approach permits to benefit in many cases, e.g., if only one straight line feature is available, it could be used by the system. This method is especially interesting to provide an estimation of the accuracy of the model without needing a large set of control tie points and with a better spatial distribution. Table 3 shows results (mean error) obtained for the previous examples. Polynomial approach Polynomial + local approach Polynomial + local approach with DEM Mountainous area (ca. 5500 points) 8.3 3.8 1.0 Cassini map (ca. 2800 points) 3.7 3.4 - Table 3. Mean coregistration error (in pixels) using feature points as control tie points. In this case, linear features are used by the system as control tie points. Another mode that can be requested from the system is to use linear features as tie lines in order to improve the model. This function is implemented by an iterative process which automatically adds new tie points along a feature. This approach permits the user to visualise and control the tie points added by the system. 3. GEORIS TOOL To meet the user and performance requirements specified, an exploitation procedure has been implemented to guide the operator step by step: select the zones of interest; catch the common features; calculate the registration model parameters; assess the positioning accuracy; apply the registration model; archive and export the registered images together with the registration models and the feature attributes. On-line easy-to-use tools are available to support the operator to draw precisely the features with scrollable zoom, improve the image contrast, adjust the position of ground control points and assess the relative positioning accuracy of any pixel in the image to be registered. As GEORIS is dedicated to prepare a set of images for photo interpretation tasks, an export facility has been developed to allow either projection of the raster and vector data in a selected

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