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

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
objects, such as buildings, do not coincide on the different
images (e.g., parallel green, yellow and red edges in the upper
row of buildings). This suggests that the Instantaneous Field
of View of the sensor had a significant spectral dependence.
Unsupervised classification. In solving remote-sensing
problems, classification - sometimes combined with
contextual information - is usually expected to provide the
final answer. To increase the reliability and robustness of
classification, many researchers favor supervised techniques.
In our object recognition scheme, classification is just one of
the early vision processes that provides only partial,
incomplete information for object recognition. Since the
number of classes is usually not known a priori and no
training data is available, we employ unsupervised
classification methods.
Unsupervised classification explores the inherent cluster
structure of the feature vectors in the multidimensional feature
space. Clustering usually results in a grouping, where the
variance within a cluster is minimized, while maximizing it
between the clusters. Clusters are not intrinsic properties of
the set of features under consideration. There is a risk that,
instead of finding a natural data structure, we would be
imposing an arbitrary or artificial structure, for example, by
selecting an unreasonable number of clusters. Therefore, it is
inevitable to analyze the distribution of the classes and their
separability in feature space.
In this test, we merged the visible-NIR bands (3-10) of the
multispectral scanner data by using the well-known
ISODATA methods. At the heart of the ISODATA scheme is
an updating loop that, using a distance measure, reassigns
points to the nearest cluster center, each time the center is
moved (Nadler and Smith, 1993). Since the number of
different cover-types is scene dependent and usually not
known a priori, the dataset was classified several times with
increasing the number of classes each time. Because some of
the spectral bands are highly correlated, different band
combinations were additionally tested. Each classification
was compared with the ground truth. Additionally, the
separability of classes was analyzed. Different separability
measures are described in the literature. To find the best
definition is not a trivial task (Schowengerdt, 1997). For our
clusterings, the different separability measures (Mahalanobis,
divergence, Jeffries-Matusita, etc.) provided very similar
We obtained the best clustering results, when using the
complete 8-band dataset (see Fig. 3b, 3c). However, when
using only 4 bands, selected from different spectral positions,
still acceptable results were obtained. Six major cover types
were distinguished in the scene (Figure 3b), namely water and
roof (black, 1), roof (dark green, 2), vegetation (red, 3 and 4),
and roof and bare soil (light gray and white, 5 and 6). Using
more classes, for example ten (Figure 3c), some of the classes
were split, giving rise to new classes with relatively low
separability. Comparing the cluster maps with the aerial
photographs reveals that despite the confusion between water
and roof pixels, and bare soil and roof pixels, the boundary
between man-made surfaces (buildings, walkways, driveways,
roads) and vegetated natural surfaces is always recognizable.
Note that other boundaries, such as the ones between bare soil
and grass, and between vigorous and sparse vegetation, are
also present, even though these boundaries are not related to
any objects of interest. It is very important to emphasize that
no building or roof spectra exist, as it is well known from
previous studies. For example, the 6-class clustering classified
roof pixels into four different classes with distinctly different
spectra throughout the entire range.
To include information about the quality of the clustering in
the visual representation, we introduce the concept of weak
and strong boundaries. Weak boundaries are located between
pixels belonging to classes with low separability; they are of
secondary importance. In the 6-class clustering, all
boundaries are strong. However, the 10-band clustering
rendered 3 weak boundaries, from a total of 45. The use of
weak and strong boundaries helps considerably in organizing
and simplifying edges.
Fig. 2 a. Visible image and detected edges; b. NIR image and detected edges; c.Thermal image and detected edges.