respectively. Datasets 1 and 2 both have higher
Cm
,
Cr
, and
Ql
. It implies that the extraction of the buildings is successful
in a whole. Over 0.94 of
Cr
means that most of the buildings
were extracted, and over 0.91 of
Cm
means that only very
few false detections occurred. Of course that it is due to the
orderly ranged and similarly sized housing pattern. It also re-
flects the good capability of the proposed method in separat-
ing out the close houses. The performance on Dataset 3 is not
as good as those on Datasets 1 and 2, because the sizes of the
buildings in Dataset 3 are variable and some markers were not
extracted correctly. Nevertheless,
Ql
for Dataset 3 still reaches
to 0.76. The much higher
Cr
than
Cm
for Dataset 3 implies
fewer false alarms but more missing.
Conclusions
The objective of this work was to precisely extract houses
from
DSM
of high-density residential areas. The proposed
method aims to solve the problem of under segmentation for
touching houses caused by low quality
DSM
. To this end, we
proposed a method of marker labeling as the indicator for
house extraction. Considering that the houses are usually of
simple shapes and roofs in high-density housing areas, we
reconstruct the roof of each house as a dome through morpho-
logical operations and extract the local maximal region as the
marker. A dome is reconstructed using a collection of slices
that are derived using iterative differential openings of the
DSM
under a series of scales corresponding to the house. We
proposed a modified granulometry and associated technique
to detect these scales.
The experiments on real datasets demonstrate that the
scale detection method is robust to different data, because it
yields a scale range instead of a single value. This scale range
guarantees that at least one slice can be extracted for most of
the smaller or lower houses thus be labeled as the marker. The
evaluations for the performance shows that the quality of the
proposed extraction technique is satisfied with higher correct-
ness and completeness and fewer mistakes. Compared with
other morphology-based segmentation techniques such as the
one that Pesaresi and Benediktsson (2001) and other research-
ers proposed, our method reduces under segmentation greatly
in dense residential areas. The disadvantage is that the sparse
tiny houses may be missed because there may be no scales
corresponding to detecting them. The proposed technique can
be able to work on other data sets, including the cases of more
complex building distribution patterns.
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