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 
• 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' 
• 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 
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. 
5.1. 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). 
Test site 1 
Test site 2 
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 
h(X,Y): resulting local height estimate 
h(X,Y): local height of the filtered (f), optical (opt) and SAR 
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.

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