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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
Fig. 1. Conceptual model of computer vision in remote
The process of segmentation may imply a change of representa
tion from the digital image data structure (pixel raster) to a list
and/or graph data structure. Classification (matching) is then
performed on the objects represented in the list (graph). The
process of converting this list of classified objects into a
thematic map may again involve a change of representation
from the list data structure back to an image data structure. This
process of "visualisation" is not explicitly shown in Figure 1.
One of the main problems in the analysis model of Figure 1 is
the interdependence of segmentation and classification: As the
segmentation should produce segments (image objects) with a
meaning in the real world, classification results may already be
needed as input for a meaningful segmentation. On the other
hand, the segmentation results are required for a good
classification (e.g. using texture and shape parameters). In the
purely sequential processing scheme of Figure 1, any
problematic segmentation cannot be remedied in the
classification step. If e.g. a segment delimited in the
segmentation process encompasses areas of two different
landcover types, the subsequent classification process is bound
to fail. Segmentation and classification therefore have to be
performed in an interrelated way. One approach is documented
in Gorte (1998).
This contribution presents a different strategy for the combined
segmentation and classification for landcover mapping.
2.1. Overview of segmentation methods
Segmentation in general is the process of partitioning an image
into segments (i.e. sets of adjacent pixels) having a meaning in
the real world (see e.g. Fu and Mui, 1981, Haralick and
Shapiro, 1992, Pal and Pal, 1993, for detailed surveys). For
landcover mapping, the segments should represent different
landcover categories. The criteria for grouping pixels into
segments can be:
• spectral and/or textural homogeneity of the pixels (globally
or within one segment),
• spectral and/or textural dissimilarity of the pixels of
adjacent segments,
• simple form of the boundaries of the segments, and
• special thematic knowledge.
Various methods exist for segmentation, each of them
emphasising some of the above-mentioned aspects.
Clustering in feature space can be considered as a simple type
of segmentation. It is solely based on a global homogeneity
criterion, not on homogeneity within the segments. Also, it does
not pay attention to the criterion of dissimilarity of adjacent
segments as well as to the criterion of simple boundaries.
Clustering in feature space in most cases produces segments for
which the sum of squared spectral distances to (global) cluster
means is a minimum, whereas the sum of squared spectral
distances to the segment means is not minimised. The main
advantage of landcover mapping based on spatial clustering is
in interactive mapping, when the landcover classes are not
known beforehand. For automated procedures, the classes have
to be defined in advance anyway. In this case, the only reason to
perform segmentation by clustering may be the need for shape
parameters of the segments in classification.
Among the segmentation techniques working in image space,
one may distinguish between region-based methods on the one
hand and edge-based methods on the other hand. Region-based
methods perform better in the case of noisy images, where it is
more difficult to detect edges. Typical region-based
segmentation techniques are split-and merge methods and
region growing methods. It is also possible to regard region
growing as a special case of segmentation based on merging.
Region growing segmentation starts at seed pixels, which form
the initial segments. At any stage of the process, all pixels
adjacent to a segment S and not yet assigned to another segment
are tested for similarity to the segment S. If this similarity is
high enough, the pixel is allocated to S. The similarity may be
defined either in terms of the difference between the new pixel
to be tested and the adjacent pixel of segment S, or in terms of
the difference between the new pixel and the mean of segment
S. If there is no new pixel to be added to S, the growing of a
new segment is started with a new seed pixel. Parameters of this
region growing method are the method of selecting seed pixels
and the threshold for defining the similarity (homogeneity)
criterion. For realising texture homogeneity criteria, texture
channels produced by filtering (e.g. with a variance filter) can
be used.
Edge-based segmentation methods start with the search for
gradients as places of discontinuities in the image, assuming
that these are the locations of the borders of the segments.
Various edge detectors (gradient operators) are available, such
as the Laplace operator or the Sobel operator. The edges are
subsequently grouped, and border networks are constructed