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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
image. As a result, very small features cannot be clearly
recognized in the Ikonos image. Since this research has an
interest to evaluate the quality of the UCL building map,
rather thane the OS MasterMap®, those inherent faults of
the OS MasterMap” was removed from the UCL data, and
small polygons whose member points are less than 100
points were also excluded before the quality evaluation.
hi Jared " A
(b) OS MasterMap” ground plan
Figure 11. Building extraction result and OS MasterMap”
A number of objective evaluation metrics suggested by
Shufelt (1999) was adopted in order to provide a
quantitative assessment of the developed building extraction
algorithm. These metrics can be defined as follows:
building detection percentage (96) 2 100 x TP /(TP - TN)
branching factor = FP/TP (8)
quality percentage (%)=100XTP/(TP + FP+ FN)
where 7P (True Positive) is a building classified by both
datasets, 7N (True Negative) is a non-building object
classified by both datasets, FP (False Positive) is a building
classified only by the UCL building map, and FN (False
Negative) is a building classified only by the OS
MasterMap". Table 1 shows the pixel classification results,
and the evaluation on the UCL building map computed by
Eq. 8 is presented in table 2.
Table 1. Pixel classification results
Pixel classification Pixels
True Positive (TP) 67085
True Negative (TN) 255794
False Positive (FP) 4344
False Negative (FN) 14639
Table 2. Building extraction metric result
Building extraction metric Evaluation result
Building detection percentage — 93.92 (94)
Branching factor 0.22
Quality percentage 77.94 (%)
6. DISCUSSION
As can be seen in table 2, the proposed building extraction
technique detected building objects with 94 % detection rate
(building detection percentage), and showed 0.2 delineation
performance (branching factor). Finally, the overall success
of the technique was evaluated as 78 % extraction quality
(quality percentage). These results suggest that the
developed system can successfully acquire accurate
detection and description of building objects using Ikonos
images and lidar data with a moderate point density.
However, the UCL building map contains certain amount of
building extraction errors (FP and FN), which should be
reduced for achieving a more accurate extraction of building
objects. The errors apparent in the result generated by the
developed system can generally be divided into three
categories:
Building detection error: most of FN pixels in Eq. 8 were
generated by under-detection of the terraced houses (see
blue coloured polygons in figure 12). This problem is mainly
caused by the fact that the NDVI classification described in
83.3 tends to over-remove "building" points over those
building with long and narrow structures such as a row of
terraced houses and results in a very small “blob”, whose
member points are fewer than 30 points. This problem can
be resolved by modifying the NDVI classification from
point-wise to region-wise approach. That is, in order to
ensure larger numbers of member points are obtained,
“high-rise” points populated in $3.2 are clustered in a
number of single objects, and then a cluster-by-cluster tree
detection is made by the NDVI classification. This
modification may make terraced houses detectable since
more member points are retained.
Building delineation error: these errors are caused when
boundaries of building objects are not properly extracted by
the building description process (see red coloured pixels in
figure 12). Those errors are related to the inherent
planimetric accuracy of input data (i.e., Ikonos image, lidar
data, and OS MasterMap ), and the point density of lidar
data. Most of boundary delineation errors are deviated from
the OS reference data with one or two pixels if lidar