Beijing 2008 
AN AUTOMATIC CONTOUR BASED DETECTION OF TERRESTRIAL OBJECTS 
FROM AERIAL IMAGERY DATA 
Yuri B. Blokhinov a , Dmitry A. Gribov b , Sergei Y. Zheltov c 
State Research Institute of Aviation Systems (FGUP GosNIIAS), Moscow, Russia - 
a blokhinov@gosniias.ru, b gda@gosniias.ru, c zhl@gosniias.ru 
Commission, WG IV/3 
KEY WORDS: Automation, Feature Extraction, Image Segmentation, Contour Matching, Dynamic Programming, Object Detection, 
Recognition, Aerial Imagery 
ABSTRACT: 
The original approach to image matching is proposed. The method itself can be classified as contour matching, based on boundary 
pattern analysis technique. Contours extraction technique is reinforced by combined information from independent contours and 
boundaries of segmented regions. In this process some general assumptions and simple rules are effectively used. Boundaries of the 
regions under investigation are extracted from digital images. Then 1-D representation of contours is constructed for further analysis. 
Features of 1-D representation are used for contour matching. For robust extraction of contour features the special procedure, based 
on dynamic resampling is used. In order to avoid ambiguities the dynamic programming technique for curves matching was 
elaborated. All calculation procedures are invariant to images rotation. Finally, the developed approach is applied to the problem of 
automatic image registration for aerial images. 
1. INTRODUCTION 
Image matching or image fragment localization is the actual and 
important problems, aroused in many different applications of 
machine vision. Ecological monitoring, forestry, monitoring of 
urban areas, automatic search of remote sensing data in vast 
geospatial databases - to mention just a few applications leading 
to the problem of image matching as a problem of patch 
identification inside large images. At the same time, the 
situations when accurate information on scale an orientation of 
images is not available and distortions are not known a priory 
are not uncommon. The authors, based on various ideas, 
proposed many approaches to such matching problem but up to 
present day, this problem is far from final decision. On the 
other hand, the task under consideration should be of interest 
for wide range of specialists. 
In general, image can be represented as a set of spatially 
distributed features. Each feature is described by set of digital 
parameters to be unique. In all feature based methods (FBM) 
matching is done on extracted features as points, edges, or 
regions (Zitova, B., Flusser, J., 2003). In contrast to the area- 
based methods, the feature-based matching works with image 
information of higher level. This property makes feature-based 
methods suitable for situations when illumination conditions or 
image geometry changes are expected. 
' 
Feature based matching procedures consist of three steps: 
extracting distinct features (points, edges or regions) in the 
images separately, 
building up a preliminary list of candidate pairs of 
corresponding features based on a chosen similarity 
measure, 
deriving a final list of feature pairs satisfying the set of 
criteria. 
branch, many interesting results was obtained (Heipke, C., 1996, 
Woozug, C., 1996), each optimal to use in its specific domain. 
Here we try to develop some special type of contour based 
matching invariant to image rotation and some distortions. This 
method was elaborated mainly for automatic monitoring of 
large areas from aerial images. The characteristic property of 
the method under investigation is that dynamic programming 
(DP) technique is used for proper curves matching. All 
intermediate steps of the method are illustrated on two pairs of 
images, one demonstrating natural objects and one 
demonstrating artificial objects. 
2. FEATURES EXTRACTION 
2.1 Outline of the Registration Method 
Among the features used for matching edges (or contours) are 
of special interest mainly because of two reasons: contours are 
highly stable with respect to various kinds of geometric 
distortions and they are natural visible features for most type of 
objects in images. In view of difficulties arising in extraction of 
contours from real images, the authors tried to develop 
approach, combining primary information from both types of 
features, edges and regions. 
The line features can be the representations of object contours, 
coastal lines, rivers and roads in aerial imagery. Standard edge 
detection methods, like Canny detector and detector based on 
the Laplacian of Gaussian (Nademejad, E., Sharifzadeh, S., 
Hassanpour, H., 2008) are employed for the line feature 
detection. However, these techniques usually do not give 
contours of required quality for real images because of presence 
of discontinuities and considerable dependence on illumination 
conditions. 
Considerable efforts was done by the investigators in this