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 The main characteristics of the GEORIS geometric correction model are: • Global correction (using a classical polynomial function) • Local correction • Capability to use linear features 2.1. Global correction A first global model is calculated using an empirical approach based on tie points measured by the operator in the set of images. The tie points are used to determine the coefficients of a polynomial transformation (least squares minimisation). This model allows performing the main geometric correction. A polynomial function of order one handles similarity and affine transformations (rotation, scale, translation). A higher order polynomial permits modelling of more complex distortions, e.g. due to attitude variations. However, the use of order higher than two is dangerous; if the number of tie points is limited and/or their spatial distribution poor, it usually leads to wrong corrections. The quality of the model is usually evaluated through the use of the residuals at each tie point (error between the location of the tie point and the location given by the model). When a tie point has a larger residual than the other ones, it is usually re-visited by the operator to check whether it is due to a pointing error. However, in most of the cases, this error does not come from the tie point measurement but from local deformation (for instance, due to the relief). This type of problem can usually not be solved using this classical approach. A larger set of tie points will minimise the problem locally but will generally introduce a larger error around this area. To limit this risk, especially because the operator does not know the mathematical approach used, control tie points are required. Then, residuals are computed but the control tie points are not used in the least squares estimation. In the GEORIS system, there are in fact four categories of tie points available: • Control tie point: not considered in the minimisation process • Normal tie point: this is the usual case • Questionable tie point: the user is not confident in the location of the tie point (because of difficulties to point it in the image with a good accuracy) • Super tie point: when the user is very confident in the location of the tie point The last three categories of tie points are introduced in the minimisation process using different weights. This approach has a small overhead for the user but permits during this step the analysis of residual errors and leads to higher accuracy. At this step, a pixel of co-ordinates (Xj,Yj) is transformed in the coordinates (x i ’,y i ? ) in the other image using a polynomial function f . -> (X i \y i ’) For each tie point, the coordinates (xi, yi) in the other image are known. This permits to compute residuals defined by Eq. 1: dx I = x i -x i ’ (1) dyi = Yi-yi’ 2.2. Local correction A specific characteristic of GEORIS is the use of a local model. This model guarantees that each of the selected tie point, measured by the operator, will be perfectly fitted by the model (no positioning errors on selected tie points). This local correction is applied taking into account the tie point neighbourhood area, whose size depends of the surrounding tie point context (i.e., road crossing, bridge over a river, shoreline, etc.). Obviously, this local model permits to correct for local deformation errors such as introduced by the terrain relief. If a digital elevation model is also available, the local correction can be automatically linked to a 3-D function (horizontal position and altitude). In this case, the local model and correction are achieved taking into account the third dimension. When no DEM is available, the pixel coordinates (X^Y;) are transformed to (x;”, y^ in the other image using successively a polynomial f plus a function (called local correction): Xi,Yj —*■» (xr,y^ — l i-> (xj-yfl The function /. depends on the location of pixel X^Y,. For a tie point Xj.Yj, it is a translation corresponding to the residuals (dx^dyO, see Eq. 2. Xi” = Xi’+ dxi (2) yi”=yi’+dyi For another point (X^YO of the image and its transformed coordinates (x i ’,y i ’), the translation dx^dy, is defined by: dx i =^Pk^ dx k / Pk (3) k <>»= Zp* Z^* 7 Pt k where: k: number of tie points p k : distance between (x i , ,y i ’) and the tie point (x k ,y k ) dx k : residual value (along x axis) of the tie point k dy k : residual value (along y axis) of the tie point k This means, that the translation depends on existing surrounding tie points, each with a weight depending on its distance from the currently processed tie point. This approach has been shown to be interesting in many cases. Table 1 shows the results of two typical cases where this approach is especially useful. The first one corresponds to results obtained at a mountainous area (using SPOT images (Xi,Yj) L

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