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New perspectives to save cultural heritage
Altan, M. Orhan

CIPA 2003 XIX th International Symposium, 30 September - 04 October, 2003, Antalya, Turkey
photographs of Alsatian façades. These images present also a
small spectral resolution for a very high spatial resolution.
The aim of this analysis is to extract the elements composing
the half-timbered façades by developing specific rules, which
should be reliable to each typical Alsatian façade.
Object of interest is the Alsatian house, built in the 18th
century. These typical residences can still be found in little
villages of Alsace or in the old part of Strasbourg city.
Only a few number of today’s carpenters are able to master the
technique and knowledge allowing the construction of Alsatian
half-timbered houses in the way our ancestors worked (Fig.l).
Figure 1. First stage in the construction of an
Alsatian half-timbered house
Several digital photographs have been taken with a conventional
camera. We used one of them - a typical (renovated) Alsatian
house - for all experiments and the others for assessing the
quality of the semantic rules.
Façades of Alsatian half-timbered houses are composed of large
and dark oak beams which constitute the structure of the house
at the beginning of its construction. Between these beams,
brickwork with facing replaces today the cob used by our
ancestors. Whatever it is, the last coat is a light colour. So, in a
first level of our experiment, we use for discrimination only the
spectral signature of the pixels.
3.1. Supervised classifications
The choice of training samples is limited to six main classes of
interest : sky, oak wood beams, freestone (sand stone), tiled
roof, shutter, and facing (over masonry). A minimum distance
as well as a maximum likelihood classification are performed.
The first one uses a geometrical algorithm simply based on the
Euclidian distance separating the pixel from the sample clusters
in the spectral space, whereas the second one is more
sophisticated since it is based on the probability (calculated
according to sample statistics) for a pixel to belong to a class. It
is certainly the most used classifier for remote sensing purposes.
The main difference between both classifications is the tiled
roof which appears more homogeneous in the maximum
likelihood (see Fig.3 b) and c)).
The other misclassifications are also mirrored by 2D scatterplots
(Fig.2). Indeed, some classes like freestone, facing and also
tiled roof present considerable spectral signature overlaps.
Figure 2. 2D scatterplot of the image (Bl=red; B3= blue)
with the 6 samples chosen for the experiment
3.2. Unsupervised classification
In order to test the presence of spectral classes, we start an
unsupervised classification (isodata scheme). It is performed
after imposing a maximum number of 6 classes. Nevertheless,
only 5 clusters appeared (not tiled roof class) as shown in
Fig.3a). This emphasises the main problem we are confronted
with, i.e. attempt to define semantic classes on the basis of
spectral classes.
Quantitatively, in order to assess accuracy, we perform a
confusion matrix using a reference classification and achieve:
77% overall accuracy for the classification with minimum
distance, 81% with maximum likelihood and 17% with
unsupervised classification. Results are not really satisfying and
above all they depend strongly on the samples.
Qualitatively, it becomes clear that conventional classification
methods are not efficient with HR imagery. Indeed, a lot of
isolated single classified pixels are dispersed over the image
(Fig.3 a) to c)). Although a majority filter could lessen this
effect, the main problem remains: a spectral analysis alone is
not powerful to discriminate classes with similar responses and
different meanings.
4.1. Supervised classification
The object oriented image analysis system eCognition2.1
(Definiens) follows the concept that semantic information
necessary to interpret an image is not represented in individual
pixels, but in meaningful image objects and their mutual
relations. That’s why image objects are generated through
multivariate segmentation where simultaneously spectral and
shape heterogeneity criterions can be weighted. A scale
parameter also influences the final object size by stopping the
merging process.
In a first step we decided to use the same training samples as in
the previous experiment in order to assess the influence of
objects instead of pixels. The sample-based classifier used is a
fuzzy approach of nearest neighbour clustering. When
activating only spectral feature as discriminating criterion, this
classifier is nearly comparable to minimum distance. Thus the
results are similar except for the homogeneity aspect (Fig.3 d)).