The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
The laser distance measurement enables the distance between 
the reflective surface and aircraft to be derived. The elevation 
of reflective locations can be calculated very exactly in the 
geodetic framework. This is because the exact position and 
flight direction of the aircraft can be determined by a Global 
Positioning System (GPS) and an Inertial Navigation System 
(INS) (Wehr and Lohr, 1999). Additional alignment parameters 
are needed to calculate the coordinates in a laser scanner 
relevant coordinate system. 
2.3 Multi- and Hyperspectral Scanner 
Measurements depend on illumination conditions, topography 
and angle-dependent surface reflection features. 
The aim of using these sensors is to derive special objects (plant 
cover, crop) and object features of the surveyed areas by 
analysing spectral surface reflection. For this purpose, common 
remote sensing and image processing techniques (e.g. 
segmentation and classification) are applied. 
Multispectral scanners possess 10 bands to 12 bands (e.g. 
Landsat TM); hyperspectral scanners possess more than 100 
bands (e.g. HyMap). Hyperspectral scanners (imaging 
spectrometer) measure object-specific signatures with high 
spectral resolution. It permits the recording of an almost 
continuous spectrum for every image element. Thus, objects 
detected on the earth’s surface are separable and thus 
classifiable. These objects exhibit characteristic absorption and 
reflection features in very narrow spectral bands which cannot 
be resolved by conventional sensors. However, the spatial 
resolution is restricted for energetic reasons. 
2.4 Radar sensors 
Based on the spectral range used these sensors also work in 
cloudy conditions. With the aid of radar sensors reasonable 
information can also be obtained from territories with extremely 
low contrast such as the ice areas of the Arctic. 
Interactions between radar signals and researched objects 
(reflection features and penetration depth) are determined by 
the used frequency and polarisation of the radar signal. 
Interferometric Synthetic Apertur Radar systems (InSAR) are 
based on the analysis of phase differences between two SAR- 
datasets taken from different positions. Because of the reference 
from phase difference to ground level high-resolution digital 
elevation models (DEM) can be generated (TSGC, 2004). 3 
3. FUSION METHODS AND TECHNIQUES 
In the following some fundamentals about fusion in relation to 
photogrammetry and remote sensing as well as relevant 
methods and applications are described. 
3.1 Fusion 
Within data processing various different algorithm or special 
software are applied to obtain derived information from raw 
sensor data. So objects and their features can be derived from 
image data by segmentation algorithms, and the behaviour of 
these objects in the surveyed area can be described. Based on 
this information, decisions can be made. Each processing step is 
equivalent to an increasing information extraction level. Fusion 
with other sensors is possible on each level. 
In this paper, pixel-, feature- or decision-level techniques are 
subdivided (Klein, 2004) (see figure 1). 
• Pixel-level fusion: combination of raw data of 
different sensors, or sensor channels within a common 
sensor to one single image (e.g. pan-sharpening of 
Landsat imagery). 
• Feature-level fusion: requires extraction of different 
single features from each sensor or sensor channel 
before merging them into a composite feature, 
representative of the object in the common field of 
view of the sensors (e.g. Tracking). 
• Decision-level/information-Level fusion: combination 
of the initial object detection and classification results 
by the individual sensors to obtain a merged product 
or even a decision by a fusion algorithm. 
Figure 1. Pixel level, feature level and decision level fusion 
The merged dataset has higher information content than each 
individual source image of the considered scene. Due to the 
competing or complementary information the result will 
necessarily possess a greater richness in detail. The images are 
to be co-registered prior to the fusion. This applies to both to 
the spatial and the temporal aspects. Image fusion of multi 
sensor images is of great importance for earth and space 
observation, especially for mapping in the fields of environment, 
agriculture and oceanography. 
3.2 Applications and methods 
Applications for sensor and data fusion are environmental 
monitoring, object recognition and detection as well as change 
detection (e.g. Sault et al., 2005; Hill et al., 1999; Duong, 2002; 
Schatten et al., 2006; Bujor et al., 2001; Madhavan et al., 2006). 
Most of the applications and methods in photogrammetry and 
remote sensing are based on pixel level fusion. Only tracking 
can also be carried out at feature level. 
Typical methods and techniques concern the improvement of 
data (resolution enhancement, bridging data gaps of other 
sensors), the combination of image data and elevation or 
distance data (orthophoto generation), the combination of high- 
resolution panchromatic and lower-resolution multispectral data 
(pan-sharpening) as well as detection and tracking within 
observed areas. 
Initially, pan-sharpening (e.g. the integration of high-resolution 
SPOT data and multispectral Landsat data) took centre stage. 
Vijayaraj performed a quantitative analysis of pansharpened 
images and presented a review of common pan-sharpening 
algorithms (Vijayaraj et al., 2006). High-resolution 
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