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Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
is restricted to bands that are spectrally located within the
spectral range of the panchromatic image. An interesting
approach has been presented by Zhukov et al. (1995), which is
based on the retrieval of spectral signatures, which correspond
to constant grey levels in the panchromatic image. The result
reveals sub-pixel variations in the multispectral bands, which are
associated with grey level variations in the panchromatic image.
Most promising are methods that use wavelet transforms for
fusion of multiresolution images, as they preserve the spectral
characteristics of the fused image to a high extent (Ranchin and
Wald, 1993, Garguet-Duport et al., 1996, Yocky, 1996, Wald et
al., 1997). In this paper, we will present an alternative method
that is based on adaptive filters.
Fusion of multiresolution optical image data aims at the
derivation of multispectral images providing the high spatial
resolution of the panchromatic image. The perfect result of such
a process would be an image, that is identical to the image the
multispectral sensor would have observed if it had the high
resolution of the panchromatic sensor (Wald et al., 1997). Such
an image would allow differentiating at least all objects that are
detectable in the panchromatic image. An approximation of the
desired result could be obtained by first extracting the objects
from the panchromatic image and „filling“ them with the
corresponding average multispectral information. Similar
techniques have been successfully used for integrating spectral
image data with vector layers in a GIS (Janssen et al., 1990).
The drawback of this method for multi-image fusion lies in the
requirement of a consistent set of object borders. Although the
requirement can be fulfilled through segmentation of the
panchromatic image, it would need computationally intensive
and therefore time consuming preprocessing. As an alternative,
we propose a filtering approach, called Adaptive Image Fusion
(AIF), that uses local object edges instead of image segments.
2.1. Sigma filter
We assume that an image object Z is represented by a set of
neighbouring pixels Zj, whose values are Gaussian distributed,
i.e. z ~ N (|i z , o z ). This assumption of normality is generally
reasonable for common spectral response distributions
(Lillesand and Kiefer, 1994). In the local neighbourhood of an
object edge we will therefore find two distributions, each
representing one of the neighbouring objects. To separate these
objects, i.e. to assign each pixel to one of these objects, adaptive
filter techniques can be used. These filters have been
successfully applied to image data for noise reduction, in
particular for suppression of speckle in SAR imagery. We have
chosen a modified sigma filter as it matches the assumptions
given above.
The sigma filter averages only those pixels in a local window,
which lie in a two-sigma range of the central pixel value (Lee
1983). All other pixels are assumed to belong to another
distribution i.e. they represent a neighbouring object. As this
filter is based on the assumption that the central pixel is in fact
the mean of its Gaussian distribution, it might not include all
relevant pixels in the averaging process. Therefore, a more
general approach, namely the modified sigma filter, was
presented by Smith (1996). It averages all pixels, which could
belong to the same distribution as the central pixel without
knowing the actual mean of this distribution.
Application of the modified sigma filter requires the estimation
of the normalised standard deviation which can be based on
empirical analysis of the panchromatic image (Smith 1996).
First, a local average and a local standard deviation image are
computed from the original image, using the window size
chosen for the filter process. Next, the standard deviation image
is divided by the average image on a pixel basis resulting in a
normalised standard deviation image. The mode of the
histogram of this image is an adequate estimate to start with,
although test runs have shown that in most cases it has to be
reduced to get the desired results.
When applying the modified sigma filter, areas with a low
standard deviation, i.e. areas containing one object, will be
smoothed. Within areas of high standard deviation, i.e. areas
containing object edges, only those pixels will be averaged that
belong to the same distribution as the central pixel.
2.2. Adaptive image fusion
For the fusion approach, the multispectral bands are included in
the filtering process. As most fusion techniques, AIF requires
co-registration of the panchromatic and the multispectral images
and nearest neighbor resampling of the multispectral bands to
the higher spatial resolution. There is no need of applying a
higher level resampling process, such as cubic interpolation, as
the blockiness of the lower resolution images will be eliminated
during the fusion process. In the following, the new high
resolution pixels of the multispectral bands are addressed as sub
The AIF algorithm starts with applying a modified sigma filter
to the panchromatic image. At each position of the moving
window the two sigma range related to the central pixel is
calculated, and all pixels within the window which fall into that
range are selected. The position of the selected pixels is then
transferred to the multispectral band, where the averaging of the
respective sub-pixels is performed. The process could be
described as sigma filtering of the multispectral band where the
filter behavior is controlled by the panchromatic image. It is
important to note that no spectral information is transferred from
the panchromatic image to the multispectral band during the
whole procedure. This leads to a better delineation of objects in
the multispectral band without significantly changing the
spectral information.
The effect of the AIF is shown in Figure 1. The figure on the left
side represents an (idealised) panchromatic image, the figure in
the center the respective (idealized) multispectral band. It is
obvious that the spectral correlation between the two images is
relatively low (as occurs e.g. with a panchromatic image and a
near infrared channel). By applying the AIF iteratively, the
mixed pixels of the multispectral band will be separated step by
step into the single objects they are composed of. The right