ng
copic mode.
ents, as well
thm and ob-
using wave-
present the
es template
ly identified
rced to pro-
ject outline
ng provides
introduction
1981]:
lity in prop-
out particu-
termination,
1 at reliable
cision in de-
ite approxi-
able.
arators from
mplex com-
[Suetens et
'al methods
xt extraction
netric semi-
TING
lentification
task of a type | operator is performed manually on a
single image, while a special automated digital mod-
ule performs the tracking task of a type Il operator.
More specifically, a human operator is used to iden-
tify an object from an on-screen display of a digital im-
age, select the particular class to which the current
object belongs (e.g. road, house etc.) and provide a
rough approximation of the object outline. Typically,
this approximation consists of loosely identifying on-
screen nodes (e.g. corners for houses, breakpoints
for curvilinear objects etc.) of the outline. Subse-
quently, these pieces of information are used as the
necessary approximations for the automatic, precise
edge positioning task. By repeating this process, any
objects within an image can be identified and pre-
cisely positioned. The degree of automation varies
according to the extent of the required human opera-
tor contribution (e.g. how many nodes have to be pro-
vided for successful object extraction and how close
to the actual outline breakpoints).
Judging from experience in both analytical photo-
grammetric data collection and digital image feature
extraction, such use of a human operator within the
broader object extraction strategy is considered opti-
mal. Humans perform the identification task flawles-
sly and almost effortlessly, and thus, their intervention
optimizes achieved accuracies without imposing time
burden. At the same time, the task of precise object
outline positioning and tracking, which experience
Shows to be the most time-consuming and error-
prone part of photogrammetric data collection, is per-
formed automatically in a fast and objective manner.
In the next sections we will present the mathematical
foundation and implementational issues for the fol-
lowing semi-automatic object exraction methods:
road extraction from wavelet transformed SPOT
imagery,
OU the use of active contour models (snakes) for
outline extraction,
least squares template matching, and
Q globally enforced least squares template match-
ing.
3. ROAD EXTRACTION USING WAVELET
TRANSFORMED SPOT IMAGES
The semi-automatic strategy for the extraction of
road networks from SPOT images combines a wave-
let decomposition for road sharpening with a linear
feature extraction algorithm based on dynamic pro-
gramming (Fig. 1).
Wallis filter preprocessing is used to enhance the
available image and facilitate subsequent object
Image Preprocessing by Wallis Filter i
Road Sharpening by Wavelet Transformation i
Manual Selection of Seed Points a
Road Detection & Tracking by Dynamic Programming
Fig. 1: Semi-automatic road extraction strategy
extraction processes by locally forcing the gray value
mean and contrast (dynamic range) to fit certain tar-
get values. Filtering is performed in image blocks of
kx I pixels by modifying the original gray value g(i.j)
of a pixel to a new gray value f(j,/), as
[g(,j) - mg cs,
(i,j) =
+ bm + (1-b) m, (1)
where m.,, m, are the old (original) and new (target)
block mean gray values respectively and s S, are
the old and new block gray value standard devia-
tions. The histogram enhancement parameters c and
b, which respectively affect contrast and brightness,
are selected such that image noise is limited. A gray
value transformation for any pixel is performed by bi-
linearly interpolating the filtering parameters of its
four neighboring image blocks [Baltsavias, 1991].
After preprocessing, wavelet transformation is ap-
plied on the image to emphasize features of interest,
while suppressing other details [Grossmann & Mor-
let, 1984], [Mallat, 1989]. A particular wavelet ¥() is
uniquely defined in the frequency domain by specify-
ing its associated filter function H(w) as
© ©
V(o) = G(3) (>) (2)
where
®(®) = [I H(27Po) (3)
p=1
G(o) = 6 “H(o+n) (4)
147
