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

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
• if the surrounding heights are also error affected (e.g. due to
interpolation from mismatched points), the spike will be
replaced by an erroneous value - a small ‘peak' or ‘hole'
remains.
• as the filter is not terrain selective, natural terrain variations
are misinterpreted and removed.
• also valid heights are randomly replaced by the median
value of the filter window, a problem, which increases with
the size of the window.
Honikel (1998) has introduced a different approach for terrain
filtering, where the most error-affected bands of stereo and
InSAR DEMs are filtered. It has been shown, that error
behaviour of both DEMs in the spatial frequency domain is
complementary, which means that the filtered erroneous parts
can be replaced by those of the counterpart. In addition, the low
systematic error of the optical DEM is preserved in the fused
DEM. This terrain selective method reduces drastically the
amount of gross errors and gives a smooth approximation of the
terrain. The performance in terms of root mean square error
(RMS) is comparable to that of the median filtered stereo DEM.
But, as it reduces the amount of large errors more effective than
the median filter, it is the preferred method for the proposed
fusion method.
It has been mentioned that statistic errors may also occur in
highly correlated areas. It is necessary to localise them, as far as
possible, before the fusion and give them zero weight, as they
would otherwise unrestrictedly enter the fusion process because
of their high correlation values. In the stereo case, the
presentation of errors as spikes, and in consequence the rough
terrain appearance, is utilised for error detection. Assuming a
smooth terrain, the local variance indicates variations and is
used to detect spikes by their locally high variance values,
which distinguishes them from valid terrain slopes, which
appear as linear features. Such identified high variations and
their adjacent neighbours are rejected by giving them zero
weight. Beside the spikes, single high correlation values in areas
of low correlation are rejected from data fusion. In the InSAR
case, also isolated points of high correlation have been
removed. In addition, values neighbouring areas of low
correlation are weighted with zero, as errors may leak out of
those areas, depending on the unwrapping algorithm (see
above).
The squared correlation coefficient puts stronger emphasis on
high correlation values. Heights with correlation coefficient
below 0.5 will be rejected from the fusion process. The filtered
DEM is introduced into the data fusion process with a general
weight of 0.5.
The proposed method will especially reduce statistic errors
occurring in both DEMs, due to its averaging properties.
However, it performs more selective than the simple averaging
of both DEMs would do, as it uses weights to distinguish valid
heights from less reliable for the computations. Regions, where
measurements performed well, are emphasised, while suspicious
areas are less emphasised and will be adapted to the data from
the other sources.
5. TEST DATA
5.1. Dataset
The test dataset consists of a pair of ERS-1 SLC quarter scenes
(Frame 819, Quarter 1, Orbit 829 and 872), taken in a three day
interval (9/12/ and 9/15/91), and a SPOT stereo pair, taken in a
four day interval (9/3/ and 9/7/86). Both datasets are part of a
remote sensing dataset of Catalonia used for our work within
the EU concerted action ORFEAS (Optical Radar sensor Fusion
for Environmental Applications). Two test DEMs, each
covering a region of approximately 50 km“ (grid spacing: 30m,
each test site 65536 points), have been generated from both
datasets in an area south of the city of Lleida, close to the
villages Llardecans (test site 1) and La Granadella (test site 2).
Further details are given in Table 1. The cartographic projection
is UTM zone 31 and the ellipsoid used is the Hayford ellipsoid
with datum in Potsdam (ED50).
Xmin,
Xmax
(m)
Ymin,
Ymax
(m)
Zmin,
Zmax
(m)
Test site 1
291030
4582680
194
298710
4590360
445
Test site 2
302430
4580880
289
310110
4588560
599
The filtered DEM is refined with the weighted average of the
InSAR and stereo DEM. The squared correlation coefficient of
each point is used as weight. Thus
h(X, Y) =
h f(X,Y)p f 2 +h opt {X,Y)p opt 2 +h sar (X,Y)p sar 2
2 2 2
P f + Popí + P sar
(4)
with,
h(X,Y): resulting local height estimate
h(X,Y): local height of the filtered (f), optical (opt) and SAR
DEM
p: correlation coefficient
Table 1. Test site data.
The terrain in both sites is undulating with height differences of
251m and 310m, respectively. In test site 1, the terrain changes
smoothly, leading only to sporadic layover, while in test site 2
various slopes steeper than 21°, the incidence angle of ERS,
occur. A DEM, derived from the contour lines of a 1:5,000
topographic map, served as reference for our computations. The
RMS of the reference DEM was approximately lm. Permanent
crops is the dominant landuse in both sites, while site 2 shows
additional coniferous forests, which bias the measurements in
the optical case.