вядемикг
In: Paparoditis N., Pierrot-Deseilligny M.. Mallet C„ Tournaire O. (Eds). 1APRS. Vol. XXXVIII. Part ЗА - Saint-Mandé, France. September 1-3. 2010
SEGMENTATION OF NETWORKS FROM VHR REMOTE SENSING IMAGES USING A
DIRECTED PHASE FIELD HOAC MODEL
Aymen El Ghoul, Ian H. Jermyn and Josiane Zerubia
ARIANA - Joint Team-Project INRIA/CNRS/UNSA
2004 route des Lucioles, BP 93, 06902 Sophia Antipolis Cedex, France
{aymen. el_ghoul, ian. j ermyn, j osiane. zerubia}®sophia. inria. f r
Commission III, WG III/4
KEY WORDS: shape priors, higher order active contours, phase fields, segmentation, road and hydrographic network
ABSTRACT:
We propose a new algorithm for network segmentation from very high resolution (VHR) remote sensing images. The algorithm
performs this task quasi-automatically, that is, with no human intervention except to fix some parameters. The task is made difficult
by the amount of prior knowledge about network region geometry needed to perform the task, knowledge that is usually provided by
a human being. To include such prior knowledge, we make use of methodological advances in region modelling: a phase field higher-
order active contour of directed networks is used as the prior model for region geometry. By adjoining an approximately conserved
flow to a phase field model encouraging network shapes (i.e. regions composed of branches meeting at junctions), the model favours
network regions in which different branches may have very different widths, but in which width change along a branch is slow; in which
branches do not come to an end, hence tending to close gaps in the network; and in which junctions show approximate ‘conservation
of width’. We also introduce image models for network and background, which are validated using maximum likelihood segmentation
against other possibilities. We then test the full model on VHR optical and multispectral satellite images.
1 INTRODUCTION
In this paper, we address the problem of road and hydrographic
network segmentation from VHR optical and multispectral im
ages: given an image 7, we seek the region R in the image do
main Q that contains the network. We would like to perform this
task quasi-automatically, that is, with no human intervention ex
cept to fix some parameters. Such segmentation problems remain
challenging due to a combination of difficulties. First, the net
work is usually not distinguishable from the background using
image measurements alone. Rather, knowledge of the geomet
ric properties of R (e.g. , that it is composed of branches that
meet at junctions) is necessary for successful segmentation. Cur
rently, this knowledge is provided, in one way or another, by a
human being. Automation of the segmentation process therefore
requires models that incorporate this knowledge of region geom
etry. This is a nontrivial matter, particularly since the regions
corresponding to networks have huge variability in their topol
ogy as well as their geometry. Second, there is great variability in
the appearances of network and background from one image to
another. Third, models incorporating the necessary prior knowl
edge of region geometry are complex, and this leads to efficiency
issues when confronted with the large size and number of images
to be processed.
The contribution of this paper is a new algorithm for road and hy
drographic network segmentation from VHR remote sensing im
ages of rural and non-urban areas which present many occluded
parts of the network entity to be extracted. Our new algorithm
uses recent advances in shape modelling allowing the incorpora
tion of sophisticated prior knowledge about network region ge
ometry, thereby addressing the first difficulty. A ‘phase field
higher-order active contour’ (‘phase field HOAC’) model of di
rected networks (El Ghoul et al., 2009b) is used to favour regions
composed of branches that meet at junctions. The network con
tains a ‘flow’, which is approximately conserved. This means that
the width of each branch changes slowly, while different branches
can have very different widths; that branches tend not to end; and
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that, at junctions, there is approximate ‘conservation of width’:
for example, several small incoming branches combining to form
a larger outgoing branch. These characteristic geometric prop
erties are different from those of road networks in VHR images
of urban areas (Peng et al., 2010), and from those of networks
in medium resolution images (Rochery et al., 2005); the problem
therefore requires the use of a new model.
In (El Ghoul et al., 2009b), the model used here was described,
but the automatic parameter setting described herein was not used,
and the model was not applied to or tested on real images. Real
images generate the second difficulty described above. To ad
dress it, we also propose generic models for the image in the net
work region and in the background whose parameters can easily
be learned from examples of these two classes. We test these
models on several VHR images. The image models are com
pared to other possibilities using maximum likelihood (ML) clas
sifications. They outperform standard indices, which suggests
that their performance when combined with the region geometry
model will also be superior. We then test the full model, com
bining the phase field model of directed networks with the image
models, on several satellite images. The segmentation problems
involved are very hard, but the results show that the new algo
rithm is able to ignore confounding factors in the background
due to the sophisticated knowledge of region geometry it con
tains, and is able to complete the network over reasonable gaps.
Before going on, it is useful to formalize the problem and our ap
proach to solving it, and to introduce some notation. We seek to
infer the region R containing the network from the image data I
and our prior knowledge K, e.g. of image formation, network
geometry, and so on. In other words, we wish to construct a
probability distribution P(R\I,K) for the region R containing
the network, given the current image data I and our knowledge
K. As usual, this can be written as the product of a likelihood
P(/|/?, K), which models the images we expect to see given that
R C il corresponds to a network, that the image is a VHR op
tical image, etc.; and a prior P(7?|A'), which incorporates our
