Full text: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
data, the images have to be geometrically and radiometrically 
corrected, before being suitable for the fusion process, using 
collateral data such as atmospheric conditions, sensor viewing 
geometry, ground control points (GCPs), etc. (Pohl, 1996). An 
elementary pre-processing step is the accurate co-registration of 
the dataset, so that corresponding features coincide. A general 
description of the necessary pre-processing steps can be found 
in Cheng et al. (1995), Toutin (1994) and Richter (1997). An 
overview on the concepts of fusion is given in Wald (1998a). 
Fig. 1. Overall data fusion process in remote sensing. 
Depending on the processing stage at which data fusion takes 
place, it is distinguished between three different fusion levels: 
• pixel, 
• feature, and 
• decision level. 
Image fusion mostly refers to pixel-based data fusion, where the 
input data is merged applying a mathematical algorithm to the 
coinciding pixel values of the various input channels to form a 
new output image. The concept of the different fusion levels is 
described in more detail in Pohl and van Genderen (1998) and 
Pohl (1998). An overview on the different definitions of data 
and image fusion is available in Wald (1998b). 
Once the alignment of the dataset is established, it is possible to 
apply certain fusion techniques. The manifold fusion techniques 
can be grouped into 
1. Colour related techniques and 
2. Statistical/numerical approaches (Pohl, 1996). 
The first group comprises methods that refer to the different 
possibilities of representing pixel values in colour spaces. An 
example is the Intensity (I) - Hue (H) - Saturation (S) colour 
transformation. The IHS technique intends to separate different 
characteristics of colour perception by the human interpreter. 
The intensity relates to the brightness, hue represents the 
dominant wavelength, whilst the saturation is defined by the 
purity of the colour (Gillespie et al., 1986). If a multispectral 
image is transformed from the RGB space into HIS, it is 
possible to integrate a fourth channel exchanging it with one of 
the elements obtained (I, H or S). There are many other 
techniques that follow the substitution principle. A description 
can be found in Shettigara (1992). Of course, there are other 
colour transformations which suit the fusion concept (e.g. RGB 
or Luminance/Chrominance - YIQ). 
The second group of fusion techniques deals amongst others 
with arithmetic combinations of image channels, Principal 
Component Analysis (PCA) (Singh and Harrison, 1985) and 
Regression Variable Substitution (RVS) (Shettigara, 1992; 
Singh, 1989). Fusion by band combinations using arithmetic 
operators opens a wide range of possibilities to the remote 
sensing data user. Image addition or multiplication contribute 
to the enhancement of features, whilst channel subtraction and 
ratios allow the identification of changes (Mouat et al., 1993). 
The Brovey transformation forms a particular method of 
ratioing, preserving spectral values, while increasing the spatial 
resolution. The PCA and similar methods serves the reduction 
of data volume, change detection or image enhancement. RVS 
is used to replace bands by linearly combining additional image 
channels with the dataset. An overview of existing techniques, 
as well as a comprehensive description of their use is given in 
the review by Pohl and van Genderen (1998). 
Image fusion is used in a broad variety of applications: geology, 
landuse / agriculture / forestry, change detection, map updating, 
hazard monitoring, just to name a few. However, in many cases 
it has not reached an operational status, due to the difficulty of 
generalizing image combinations and fusion processes. Due to 
the scarcity of simultaneously acquired satellite imagery, most 
image fusion applications carry a multi-temporal component. In 
some cases, it is used in the framework of monitoring (change 
detection); in others it is an unavoidable constraint and has to 
be considered in the evaluation of the resulting fused product. 
A very important factor of applying image fusion is the 
integration of complementary data. The complementarity of 
visible and infrared (VIR) with synthetic aperture radar (SAR) 
images is a well known example, where the objects contained in 
the images are ’seen’ from very different perspectives 
(wavelength and viewing geometry). The integration of high 
resolution and multispectral information forms another type of 
The following sections provide an overview of issues in 
operationally-used image fusion relating to the processing 
involved and discuss benefits and limitations of approaches, 
illustrated by applications. All results have to be viewed in the 
context of visual image exploitation. 
3.1. Resolution Merge 
One of the more established approaches is the so-called 
resolution merge. It aims at the integration of lower resolution 
multispectral with higher resolution panchromatic data in order 
to benefit from high spectral and spatial resolution in one 
image. It is relatively straightforward, when using data from the 
same satellite, e.g. SPOT PAN & XS, IRS-1C PAN & LISS, 
etc. But it is as well applicable to imagery originating from 
different satellites carrying similar sensors (e.g. SPOT XS &

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