International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999

many years and many applications. However, polynomial

transformations should not be taken into consideration for

precise image registration. In some cases, polynomial

transformations can be used, e.g. an affine one could be used to

transform an already geocoded image from one UTM zone to an

adjacent one, or to register multi temporal images acquired by

the same sensor under very similar imaging conditions, like it

happens for Landsat TM. They can also be used, if the terrain is

not rough and can be approximated by an appropriate

polynomial.

Polynomial and image warping methods are based on the use of

2-D tie-points between datasets to be registered. Image

distortions caused by the terrain topography are usually not

taken into account. Following for instance a geometric

interpretation of a polynomial-based rectification, an image is

mapped onto an artificial “tie-point”-surface defined by the

points being used. This may cause severe displacements in

comparison to a precise orthorectification of the image onto the

real terrain or the reference ellipsoid surface, respectively (see

Figure 2).

3.2, Parametric Methods

Parametric techniques are based on the use of sensor-specific

mapping models in order to relate image and map data. For

representation of the terrain surface, a digital elevation model

(DEM) is usually incorporated into these methods. These are

the essential prerequisites to establish a precise relation between

a point on ground and the related pixel in an image (Figure 3).

Images can then be registered either in image or map geometry.

Registration of images in map geometry is in general achieved

through geocoding to a common geometry referring to a defined

map projection. Registration in image geometry is achieved

through a combination of geocoding (image-to-map) and map-

to-image registration techniques. Geocoding is the standard

approach, as the output can be immediately compared to other

geocoded products.

Fig. 3. Scheme of parametric relation between image and real

topography, represented by a DEM.

3.2.1. Geometric Modelling

Parametric methods rely on the availability of a sensor-specific

imaging model. The establishment of models to map image to

object space and vice versa is called geometric modelling in our

terminology and is the essential preparatory step for further

registration or geocoding.

In the geometric modelling task, any relevant sensor-specific

parameters must be provided in order to establish a precise

relation between an image pixel and the corresponding ground

location. Such parameters include both information on the

sensor geometry (e.g. pixel size, focal length etc.) as well as the

position and attitude of the sensor during image acquisition.

Latter information is delivered as ancillary information together

with the image data for the majority of current remote sensing

sensors. The information on sensor geometry, position and

attitude is used to derive initial approximations for the

parameters of the geometric model (initial imaging model).

However, the geometric parameters being provided are

generally not accurate enough, or part of the relevant imaging

parameters is even missing. Ground control points (GCPs)

measured in the image and the reference coordinate system are

therefore usually required in order to derive approximations for

the missing geometric imaging parameters and to accurately

estimate the entire set of imaging parameters through the

application of least squares adjustment procedures. This leads to

an optimised imaging model (see Figure 4).

The accuracy resulting from optimised imaging models is

mainly driven by the localisation accuracy of the GCPs in the

image, which depends on the ability to exactly identify the