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APPLICATION OF EDGE EXTRACTION ALGORITHM IN RADAR IMAGE
ANALYSIS
N.N. Potapov1, P.A. Tupikov2
1
Nizhny Novgorod State Technical University, 24 Minina Str., Nizhny Novgorod, 603950
Russia, +7 831 236 78 80, nick.potapov@mail.ru
2 Nizhny Novgorod State Technical University, 24 Minina Str., Nizhny Novgorod, 603950
Russia, +7 831 236 78 80, ppro@inbox.ru
The paper deals with methods of edge extraction from radar image and different ways of
image comparison using edge extraction algorithm. Results of analysis of calculations
reliability with different image configuration are discussed.
Introduction
At present stand-alone systems of aircraft location definition are widely used. These systems
calculate precise coordinate using information
which was obtained from onboard navigation
complex. Navigation systems based on comparison of information from observation unit
and geophysical field sensor (relief field, radiobrightness, gravity) with corresponding information stored in onboard computer’s
memory are called map-matching systems. Data processing in such systems is performed according to algorithms, which realize calculation of mutual correlation function (or similar)
of reference information and data obtained
from field sensors. When mutual correlation
function calculation is complete search of
global extremum position of this function is
performed. Such method of navigation information processing allows providing high resolution of aircraft coordinates determination. It
should be mentioned, data from radiolocation
systems is enough to perform calculations.
Problem definition
2-dimensional arrays of underlying surface parameters obtained from onboard radar we will
call “measured map”. 2-dimensional arrays of
parameters of surface aircraft to fly above,
which is known beforehand we will call “reference map”. Measured map contains infor-
mation about measures of slant range and radiobrightness of underlying surface elements.
Reference map contains information about relief heights and radiobrightness of underlying
surface. Correlational-extremal processing
consists of comparison of measured and reference map using some algorithms. Thus reference and measured map are input data of correlational-extremal processing algorithms.
This paper deals with different modifications
of edge extraction algorithm. It’s proposed to
use this algorithm in radiobrightness data processing. Measured map contains a quantity of
measures, which is obtained in equal time interval from aircraft radar. This information is
received from three radars. One of these radars
(central one) is directed strictly down and two
others are tilted at fixed angle from center radar. All three rays lay in the same plane, which
is normal to aircraft movement direction.
Size of measured map formed by radar is
X m  Ym points. Reference map size is
X 0  Y0 points. This map is should be loaded
to onboard computer’s memory during the
flight task preparation.
Correlational-extremal processing using edge
extraction algorithms consists of several sequential stages:
 ranging of maps elements according to intensity level (ranging interval is constant for
the whole map);
 edge extracting;
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 conversion of extracted edges into the form
suitable for subsequent comparison;
 comparison of edges extracted from measured and from reference maps;
 search of maps matching point.
Sample of reference map is shown at Fig. 1.
Result of edge extracting from reference map
is shown at Fig. 2.
quential processing and some troubles with
processing of embedded figures or very short
edges. Using sequential scanning is necessary
to search objects on image.
Characteristic example of trace method is
“Beetle” method (Fig. 3). Beetle starts moving
from white area toward black area. When beetle met black point it turns left and keep moving. If the next point is white, bug turns to the
right. Beetle moving until it reaches start
point. In case of unclosed contour, beetle
moves along this contour twice. Points, where
bug turns left is the edge of searching object.
Fig. 1. Reference map
Fig. 3. “Beetle” method
Fig. 2. Edges, extracted from reference map
Sequential scanning method (Fig. 4) based on
image derivation separately in two perpendicular ways. Points, where derivative reaches defined level  2 , belong to edge.
Advantages of this method, in comparison
with other methods, are high reliability and independence between processing time and map
complexity.
Edge extracting can be performed according to
different methods. These methods differ in
calculation complexity and application area.
Let’s consider two edge extraction methods,
which are the most suitable for the problem
being discussed.
Edge extraction methods
Trace methods based on searching objects in
image, edge sensing and converting edges to
vectors. Searching objects, as used here,
means searching any one points of object.
Good advantages of these methods is simplicity and speed, but main disadvantages are se-
Fig. 4. Sequential scanning method
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In case of little transverse size of measured
map Ym , almost all edges in map is unclosed
and major part of map is boundary. Trace algorithms is not efficiency there. Transverse
size of map equals radar beam count (3 beam),
in current problem preferred solution is using
sequential scanning method for edge detection.
There are few possible ways to edge definition
(coding). Each definition method includes reference and measured map comparison algorithm.
Vector edges coding means conversion edges
to vectors. Correlation function in this algorithm equals quantity of vectors with same coordinates on reference and measured map.
Advantages of this algorithm are simple calculation, parallel calculation capability, but disadvantage is troubles in map comparison when
reference or measured map is sparsed. (see
Edge extraction comparison).
Point matching map comparison algorithm includes bit array comparison. One array contains edges from reference map, but other contains edges from measured map. Distance between neighbor points in one direction must be
the same for deriving higher reliability. In other case measured map must be scaled. Correlation function equals count of coincident
points (total element sum in product of two arrays) for each possible coordinates.
Main advantage of this algorithm is modest
requirements to keep maps in memory. So, if
map size is 512x512 pixels, to keep map needs
10 times less free memory.
Converting edges to geometrical figures is
used then reference map size and measured
map size are rather large. Disadvantage of this
algorithm modification is high complexity.
In the best way, if we take into consideration
limitations of on-board electronics, point
matching map comparison algorithm is preferable.
Edge extraction methods comparison
Let’s analyze results of calculation using edge
extraction algorithm with sparse reference
map. This sparsion is a consequence of changing cruising altitude or airspeed. In this way,
increasing cruising altitude leads to increasing
distance between contiguous measuring items
on land. In other words, this is lateral reference
map sparsion relative to course. Increasing airspeed leads to longitudinal reference map
sparsion relative to course.
In order, to make an analysis of edge extraction algorithms, we select level of relative map
sparsion and made series of N E launching.
New reference map was creating in each
launch. On reference map new coincident
point was selected and measured map was created with selected measure of discrepancy.
Then edges were extracted using sequential
scanning methods. Last stage was a map
matching image processing, which contains
point maching edges comparison. Launch
takes into consideration, when calculated
measure map coordinates coincides with selected map coordinates. N ES will be a count
of successful launches. Reliability of algorithm
can be calculated as probability of successful
N
test P  ES . Also we made an investigation
NE
of interdependence between calculations reliability and width of edges quantization level
(  ). We use following parameter values:
X 0  256 , Y0  100 , X m  50 , Ym  3 ,
N E  100 .
Conclusion
We can draw a conclusion, based on analysis
result.
 Reliability of algorithms, calculated in different launches never reaches 100% level;
 Reliability of algorithms decrease with increasing width of edges quantization level;
 Reliability of algorithms slowly increasing
with increasing lateral reference map sparsion
relative to course;
 Reliability of algorithms decreasing with
increasing longitudinal reference map sparsion
relative to course;
Derived results can be explained in the following way: reliability increasing with decreasing
width of edges quantization level takes places
because accuracy of extracting edges with minor fluctuation of map elements values. Increasing of sparsion along the direction which
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is perpendicular to aircraft course. If map size
is small it leads to reliability increase. It
should be mentioned that large measured map
size (i.e. along aircraft course) in combination
with map sparsion (along the same direction)
leads to increasing of edge extraction error
probability and degrading of reliability.
References
1. G.A. Andreev, A.A. Potapov Navigation spatialtime information processing algorithms (part 1) //
Zarubezhnaya radioelektronika (in Russian).–
1989.– No 3.– P. 3–18.
2. G.A. Andreev, A.A. Potapov Navigation spatialtime information processing algorithms (part 2) //
Zarubezhnaya radioelektronika (in Russian).–
1989.– No 4.– P. 3–21.
3. I.N.Beloglazov, V.P. Tarasenko Map-matching systems.– 1974.
4. A.M. Bochkarev Map-matching systems of navigation // Zarubezhnaya radioelektronika (in Russian).–
1981.– No 9.– P. 28–53.
5. A.G.Buymov Correlational-extreamal image processing.– 1987.
6. Map-matching systems of control: Proceedings / Edited by V.P. Tarasenko.—1981.
7. A.A. Krasovsky, I.N.Beloglazov, G.P. Chigin Theory of map-matching systems of navigation.– 1979.