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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
M. Honikel
Institute of Geodesy and Photogrammetry, ETH Zürich-Hönggerberg, CH-8093 Zürich, marc.honikel@geod.ethz.ch
KEYWORDS: DEM, ERS, SPOT, Data fusion.
Generation of digital elevation models (DEMs) is a main issue of remote sensing, as precise DEMs are needed in a wide range of
remote sensing applications. The most widespread techniques for DEM generation are stereoscopy for optical sensor images and
interferometry for SAR images. Both techniques suffer from certain sensor and processing limitations, which can be overcome by the
synergetic use of both sensors and DEMs respectively. In this paper, different strategies for fusing SAR and optical data are
combined to derive high quality DEM products. Quality in this context means accuracy of heights. An estimation of the error
properties of both datasets is derived both from the data processing and the single DEMs themselves. The stereo-optical and In SAR
techniques have distinct, partially complementary, error properties. Two techniques, which take advantage of the complementary
properties of InSAR and stereo-optical DEMs, will be applied for the fusion process. First, as cross-correlation plays a major role for
the height measurement in both techniques, the cross-correlation of phases, respectively grey values, are determined for each DEM
point and used as weight for the DEM fusion. In the second technique to be applied, by taking advantage of the fact that errors of the
DEMs are of different nature, affected parts are filtered and replaced by those of the counterpart. The results of both techniques will
be fused in a final step. This procedure is tested with two sets of SPOT and ERS DEMs of regions in south-central Catalonia,
resulting in a remarkable improvement in DEM accuracy and representation of the terrain.
The two main techniques for DEM generation from satellite
imagery are stereoscopy with optical sensors and interferometry
with SAR sensors. The height is measured in different ways in
both techniques. InSAR computes the height from the phase
difference of the point backscatter in two passes. Since phases
in an interferogram are wrapped in an 2n interval, the critical
step in the generation of elevation models with SAR
interferometry is phase unwrapping, i.e. the solution of these
phase ambiguities. Phase unwrapping becomes extremely
difficult in cases of low signal to noise ratio due to
decorrelation of the phase measurements. In the optical case, the
coordinate difference of conjugate points in both images must
be measured for the height determination. Therefore, the correct
identification (matching) of conjugate points is essential for
accurate stereo height measurements. Again, decorrelation
handicaps the matching or even leads to point mismatching.
As both techniques are based on different principles and
sensors, the question arises, how these independent height
measurements can be fused to obtain DEMs, which do not
suffer from the single sensor limitations and consist only of
valid measurements of each single source. Obviously, this task
goes beyond the straightforward (yet not simple) identification
and exchange of data in regions, where the height measurement
of one of the contributing sensors fails (e.g. occlusions,
layover), as it would not take advantage of the independent
measurements as a whole. An estimation of the height errors is
needed for any fusion process, in order to profit from both
sensors. Provided a measure is found, which is directly related
to the height error, it can be used to define a weight according
to which the DEMs can be fused. As correlation is a critical
factor for both techniques, the correlation coefficient of both
measurements will be introduced as weight for the proposed
fusion method. Although the correlation coefficient has
different meaning in both techniques, it still relates to the
measurement error.
Like all measurements, the measurement of topographic heights
is error corrupted for various reasons. According to the error
model introduced by Baarda (1967), three general types of
errors have to be distinguished:
• systematic errors, which occur in all measurements due to
measurement inconsistencies.
• random errors, which are inevitable and nonpredictable.
They can be described as random variables.
• blunders, which are large and occur due to an erroneous
handling of the measurement process.
This error model, originally designed for geodetic networks,
applies to redundant measurements of the same parameter. As
we deal here with single measurements with different sensors,
the model must be modified, as an assumption of redundancy
would be invalid in this case.
The systematic error in this context applies to a global error,
which affects all measurements within a DEM (e.g. baseline