You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

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
Fig. 9. Result of the knowledge based segmentation for the object class ‘settlement’.
Assuming that the appearance of landuse classes in satellite
images can be characterized by a significant accumulation of
pixels with typical reflectance values compared to the
surrounding areas, the spatial distance of pixels with similar
reflectance values can be a criterion to find object boundaries.
As an example, Fig. 3 shows an original detail of a Landsat TM
image and Fig. 4 the significant vegetation-free pixels (in black)
as candidates for the object class 'settlement'. A successful
method to determine the object contours is the use of
triangulation networks, e.g. Delaunay triangulation, of the
marked pixels (see Fig. 5). Based on all DLM200 settlement
objects in the satellite image, statistics of the triangle perimeters
(mean values, standard deviations) can be determined. This is
used as a structural parameter related to the 'compactness' of
the object. On the other hand, valid triangles can be extracted
by means of this perimeter statistic (Fig. 6). After a fusion of all
adjacent valid triangles, the object contours can be obtained
(Fig. 7). The same procedure can be applied to all other - also
the homogeneous ones - object classes. Fig. 8 shows the
enclosing object contours for the accumulation of the marked
pixels having a specific vegetation index and the corresponding
DLM objects in this area. The result of the segmentation
process is a symbolic scene description with polygonal objects
of the three land use classes.
In the last step of feature extraction, texture parameters inside
the object contours are calculated. A very simple but efficient
parameter is the standard deviation of the grey values inside the
objects. Because of some disturbances, for instance caused by
errors in the topographic database or errors in the geocoding of
the satellite image, a histogram analysis of the grey value
distribution is needed. In this histogram analysis, the peak of
the grey value distribution is extracted and the minor peaks are
eliminated. The standard deviation is estimated for this changed
distribution. The result is a Modified Standard Deviation,
which can be used as robust texture parameter (Fig. 10). Also
test series with the well-known texture parameters proposed by
Haralick and Shapiro (1992) have been carried out. The tests
have shown that homogeneity is one of the best parameters to
distinguish between the above defined object classes (Fig. 11).
With both texture parameters the separability of homogeneous
from inhomogeneous classes is very good. This is important for
the spectrally similar classes ‘settlement’ and ‘agriculture’.
n m
homogeneity = xi
j=l i=l
1 + (g(i) — g(j)) 2
w(i, j) ...element of the co - occurrence matrix
g(i), g(j) ...grey values in a row / column
Neigbourhood relations for an image object are determined by
storing the identification code numbers of adjacent objects.