This document presents a methodology for detecting airport runways in satellite images. The methodology consists of two main stages: 1) a segmentation stage that classifies image regions as "probably runway" or "not runway" based on textural features, and 2) a shape detection stage that analyzes segmented regions to identify long parallel lines characteristic of runway sides. The document describes various textural features extracted for segmentation, including Haralick textures, Gabor filters, Fourier spectra, and wavelet analyses. It also details an artificial neural network classifier trained on these features to perform the segmentation. Experimental results demonstrate the algorithm can successfully detect runways in satellite imagery.

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Abstract—Automatic detection of airports is especially essential, attributable to the strategic importance of those targets.
during this paper, a detection methodology is planned for flying field runways. This methodology, that operates on massive
optical satellite pictures, consists of a segmentation methodsupported textural properties, and a runway form detection stage.
within the segmentation method, manynative textural optionsarea unit extracted. Since the most effective discriminative
options for flying field runways cannot be trivially foreseen, the ANN algorithmic ruleis utilized as a feature selector over an
oversized set of options. Moreover, the chosenoptions with corresponding weights willgivedata on the hidden characteristics of
runways. The plannedalgorithmic rule is examined with experimental work employing a comprehensive knowledge set
consisting of enormous and high resolution satellite pictures and thriving results area unit achieved.
Keywords: Airport runway detection, Textural features, Segmentation, ANN algorithm.
I. INTRODUCTION
Airports are important structures from both
economical and military perspective. Economically, as
fundamental cargo and passenger transportation stations, airports
serve to attract and retain businesses with national and globalties.
Therefore, air- ports are a major force in the local, regional
,national and global economy, becoming increasingly significant
interms of financial reasons. The military airports,i.e. Airbases,
are also critical strategic targets considering the importance of the
aviation branch of a nation’s defence forces. Airbases are used
for not only take-off and landing of crucial bomber and fighter
units, butalsocon sequential support operations such as strategic
and tactical airlift, combatair drop and medical evacuation,
promoting the worth of airports .From this point of view,
automatic detection of airports can provide vital intelligence to
take well-timed military measures in a state of war. The
technological improvements on both computational hardware and
pattern recognition techniques made identification of airports an
attain able objective. Besides, increasing number of countries
that have their own satellites renders the problem even more
attractive, by the supplied un biased data to investigate. These
reasons form the motivation of this measures during a state of
war. The technological enhancements on each process hardware
and pattern recognition techniques created identification of
airports a possible objective. project. From now of read,
automatic detection of airports will offer important intelligence
to require well-timed military Besides, increasing variety of
nations that have their own satellites renders the matter even
additional enticing, by the equipped unbiased information to
research.
These reasons type the motivation of this paper. during this
letter, field runway detection is undertaken by the ANN learning
algorithmic rule [14] utilized on an oversized set of textural
options. it's used to find the most effective discriminative options
with corresponding weights, which might represent the real
native characteristics of the runway texture that can't be
intuitively identified. Additionally, Adaboost doesn't suffer from
the curse of spatiality and an over sized process price for the
extraction of intensive variety of options since it discovers that
options area unit to be employed in the classification and that
area unit to be eliminated by its feature choice property. This
strategy relies upon ending as several options as doable and
property
PROPOSED RUNWAY DETECTION
ALGORITHM
The proposed runway detection method basically consists of two
main stages, which are binary classification of regions based on
textural properties, and analysis of these regions based on shape.
In the first stage a coarse segmentation is done on the satellite
image, in order to find candidate regions for airport runway,
based on the textural properties. This segmentation is a binary
segmentation, where regions are labelled as either ―probably
belongs to a runway‖ or ―probablydoes not belong to a runway‖.
After this segmentation, only regions that possibly belong to a
runway are considered and proceed to the second stage. In the
second stage, a shape detection algorithm, which discovers long
parallel line segments, is carried out on the ―possibly runway‖
regions. These long parallel lines are considered as the
identification marks of the two long sides of the elongated
rectangle shape of the runway.
Abuthahir A Mohana Arasi M
PG Scholar/Applied Electronics, Assistant professor,
Department of ECE, Department of ECE,
Bannari Amman Institute of Technology, Bannari Amman Institute of Technology,
Sathyamangalam. Sathyamangalam.
Airport Runway Detection Based On ANN
Algorithm

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II. METHOD
First, satellite picturesar divided into non-overlappingimage
blocks of size N by N pixels. N is chosen to be thirty two that is
specified as acceptable for associate degreeairfield runway
breadth in1-m resolution pictures. Throughout the method, these
blocks, painted by f(x, y) wherever x and y represent the
coordinates of the blocks, arethought of to be the
fundamentalcomponents, and every one feature extraction and
classification operations aredead in terms of whether or not
theyare a runway or not.
A. Features
The features used in this study are explained below. Throughout
this section, the
Concerned image is represented as
f(x, y)
which is assumed to be N × N in size.
• Basic features (features F1–F4):Runways square
measuretypicallyan identicalgrey level and brighter than their
surroundings. Thus, the means thatand therefore the variances of
intensity, and therefore the gradient of intensity within the image
blocks will describe the intensity and variation, severally.
• Zernike moments (F5–F13):Zernike moments [5] ar
rotation-invariant image moments. The order of a Zernike
moment should have associate degreeboundaryto possess a
possible computation. During this letter, the Zernike moments of
order from zero to four, leading toa complete of 9options,
arthought ofin keeping with the restrictions in memory and
procedure time.
• Circular-Mellin features (F14–F23):Circular-Mellin
optionsalso are orientation and scale invariant. These
optionsprofit of 2 parameters, i.e., radial frequency and circular
frequency. Some experimental results square measure given in
[6] regardingthe choiceof those variables by a probeformula.
The selection of the set of utilized circular-Mellin options was
determinedsupported the parameters given in [6].
•Fourier power spectrum (F24–F33):The Fourier power
Spectrum is employed to extract optionsassociated with periodic
patterns. The facility spectrum of the image block are often
examined in ring- [9] or wedge-shaped [10] regions. The latter
area unit orientation dependent, and thus, they weren't used.
doughnut-shaped regions willofferinfoconcerning repetitive
forms. During this letter, power spectrum was divided into six
equal doughnut-shaped regions, and also the total powers
comprised by every region were thought-about as options.
Additionally, the utmostworth, the commonworth, and also the
variance of the distinct Fourier rework magnitude,
additionallybecause the overall power spectrum energy, were
used.
• Gabor filters (F34–F81):A wordbook of physicist Filters
with six orientations and 4 scales was used. The opposite
parameters were chosen in step with [8]. The suggests thatand
{also the} variances of the Gabor-filtered output pictures were
also used. to formphysicist filter outputs close to rotation
invariant, the feature vector is circularly shifted so the
scale–orientation try having the most mean is found at the start
of the vector[2], [10].
• Haralick features (F82–F97):Gray-level co-occurrence
matrices square measure calculated [17]. Once no previousinfois
offered, it's common to use offsets (1, 0), (1, -1), (0, -1), and (-1,
-1), that correspond to adjacent pixels at 0◦, 45 ◦, 90 ◦, and 135◦,
severally. However, we tend toat firstelitethe simplest
discriminative window size from a group of different-sized
windows (1, 3, 5, 7, and9 pixels). The chosen size was adjacent
pixels, and that we used that size for classification
analysis.FourHaralick feature (energy distinction, homogeneity,
and correlation) for four offsets (16 options in total) were used.
• Wavelet analysis (F98–F121):These optionsarea unit
expected to provide a quantitative description of the textural
properties associated witheach frequency and spatial domains. A
three-level decomposition structure was utilized, and therefore
the energies and therefore thecustomary deviations of the four
parts (low–low, low–high, high–low, and high–high) for the 3
levels were used as options, giving a complete of twenty
fouroptions.
• Features in Hue, Saturation, Value (HSV) color space
(F122–F137):Since the runways tend to be in gray tones
And colorfulness is a synonym for saturation; it is the saturation
that will most probably provide valuable information. Likewise,
the hue is closely related to the dominant wavelength, and
although it is not so evident, the dominant wavelength of the
color of a runway might be useful. For these reasons, the mean,
the variance, and the mean and variance of the gradient
magnitude, as well as the Zernike moment of order 1 and
circular-Mellin feature for both saturation and value
components, were employed. Since these two components
provide linear information, the common mean and variance
formulas still apply. On the other hand, since the hue bears
angular information, its directional statistics are involved in the
mean and variance calculations. Since the Zernike and
circular-Mellin features inherently require magnitudes rather
than angles, the hue component is not utilized for these features.
Employing features from the HSV color space for runway
detection is a novel practice, and it has been shown to be very
effective in the experimental analysis.
III. ARTICIAL NEURAL NETWORK
CLASSIFIER
The main Goal is to learn from a set of training data and to
generalize from learned instances to new unseen data. An

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artificial neural network can communicate by sending signals to
each other over a large number of weighted connections.
Technical viewpoint:
The problems arising here as character recognition or the
prediction of future states of a system requires massivelyparallel
and adaptive processing.
Artificial neural network can be used as to simulate the
components of the airports.ANN can be trained to solve certain
problems using a teaching method and sample data. The
constructed ANN can be used to perform different tasks
depending on the training received. With regular training, ANN
is accomplished with generalization, and has the ability to
recognize co-relations among different input patterns.
Fig.2. Illustration of Neural Network Classifier
IV. Flow Diagram
V. EXPERIMENT RESULT
Fig1,2: filter output

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Fig 3, 4: Runway Output
A method for the detection of airdrome runways is
projectedduring this study. This methodologyis predicated on
associate degreeapproach that involves a segmentation method
and anensuant geometric analysis on the aerial image. Within the
segmentation part, textural properties square measurethought of,
and principallycurrent textural options that square measure used
for segmentation within the literature square measureutilized.
Additionallythereto, using Artificial Neural network learning
algorithmic rule and utilization of options obtained by using’s
color area, physicist Filters, Fourier Power spectroscopic
analysis and Wavelets, are original works for the airdrome
runway detection downside. Segmentationmethodmay also be
changed with a multi-class ANN learning algorithmic rule, so it
willfunction a general purpose region of interest detector, for a
useful automatic target detection system. This improvement
provides associate degreepotencysweeteningattributable to the
unification of the detection of the regions of interest operations
for numerous targets.
VII. CONCLUSION
A texture-based technique for the detection of field runways has
been projectedduring this paper. Since it's not a trivial task to
pick discriminative options for classification, it should be
inadequate to intuitively state the discriminative options for the
classification of the objects of interest in remotely
perceivedpictures. ANN provides the
foremosthelpfuloptionswhich willconjointly bear the nontrivial
characteristics of objects. Thus, it'spotential to deduce hidden
characteristics of objects, and this represents the twofold edges
of the projectedtechnique. In general, the projectedtechniqueis
also used for other forms of objects of interest (targets) to higher
expose their hidden options. Then, domain data, if obtainable, is
incorporated with designatedoptions for target detection and
recognition .Classification isconjointlychanged with a multiclass
enzyme boost learning algorithmic ruleso it willfunction a
general region of interest detector for a useful automatic target
detection system.
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