feasibility study of mobile tower locations using classification
TRANSCRIPT
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Feasibility study of mobile tower locations using classification result of satellite image
Ajaze Parvez Khan
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What is Paper about!!
Once any network planning is completed…
We check is it possible to implement this network on ground……
Through this paper we are proposing that
FEASIBILITY can be ascertained employing
CLASSIFICATION……
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Cellular Network Planning
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Network Planning
Is this one on a river
Is this one on a restricted area
Is this one on somebody property: Our idea fails….
5
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Wireless communication is one of the most rapidly growing technologies worldwide. Various industry software like Kiema Overture , Akosim , etc are used for efficient network planning for various wireless technologies.
Owing to the high costs and the scarcity of installation sites, an accurate and efficient site location verification procedure is required.
Till date multispectral classification has never been undertaken for validation of site locations especially mobile tower locations.
An attempt has been made to employ multispectral classification technique to analyze the feasibility of installation of location for mobile communication towers thereby reduction in cost incurred on field visits to a certain extent.
Formal Statements
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Methodology
Supervised Classification of
the region
Draping of classified satellite
image of the region on this
3D Model
Feasibility analysis
(verification) of mobile
towers at these locations
D = ln(ac) - [0.5 ln(|Covc|)] – [0.5 (X-Mc) T (Covc-1) (X-Mc)]
D = ln(ac) - [0.5 ln(|Covc|)] – [0.5 (X-Mc) T (Covc-1) (X-Mc)]
Maximum Likelihood
The maximum likelihood decision rule is based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes, and that the input bands have normal distributions.The equation for the maximum likelihood classifier is as follows:
D = weighted distance (likelihood)C = a particular classX = the measurement vector of the candidate pixelMc = the mean vector of the sample of class c
ac = percent probability that any candidate pixel is a member of class c
(defaults to 1.0 for MLC, or is entered from apriori knowledge)Covc= the covariance matrix of the pixels in the sample of class c
|Covc| = determinant of Covc
Covc-1 = inverse of Covc
ln = natural logarithm functionT = transposition function
D = weighted distance (likelihood)C = a particular classX = the measurement vector of the candidate pixelMc = the mean vector of the sample of class c
ac = percent probability that any candidate pixel is a member of class c
(defaults to 1.0 for MLC, or is entered from apriori knowledge)Covc= the covariance matrix of the pixels in the sample of class c
|Covc| = determinant of Covc
Covc-1 = inverse of Covc
ln = natural logarithm functionT = transposition function
The pixel is assigned to the class, c, for which D is the lowest.The pixel is assigned to the class, c, for which D is the lowest.
Using DEALDEM* application
to find locations of mobile
towers on 3D model
*DEALDEM IS THE WEB BASED APPLICATION DEVELOPED INHOUSE FOR FINDING OPTIMAL LOCATIONS OF THE TOWERS
Dialog for 3D Controls
Dialog for inputting 3G parameters
TrueColor Image Draped over the DEM
Study Area
Results
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Results
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TOWERS
Position Installation
Status/Solution Results
A Completely sits
over Built-Up
area
Installation Possible
B Completely sits
over forest areaREJECTED
C Sits over mixed
classes/signature
area
Can be installed
(If land or building for
installation is available,
field visit required)
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Conclusions
1)In this paper it is proposed that multispectral
classification may be used as a tool to find the feasibility of
locations for human facilities.
2)The results show that multispectral classification is
effective to decide the feasibility of location.
3)The results also suggest that classification results
provide an excellent tool for validation of network planning.
4)Immense effort and resources can be saved if feasibility
of any facility location is first established on classified image
of that region.
5)For the locations that are not approved, the information
may be used as a feedback mechanism to compute the
acceptable and accurate tower locations.
1)In this paper it is proposed that multispectral
classification may be used as a tool to find the feasibility of
locations for human facilities.
2)The results show that multispectral classification is
effective to decide the feasibility of location.
3)The results also suggest that classification results
provide an excellent tool for validation of network planning.
4)Immense effort and resources can be saved if feasibility
of any facility location is first established on classified image
of that region.
5)For the locations that are not approved, the information
may be used as a feedback mechanism to compute the
acceptable and accurate tower locations.
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References
[1]. www.overtureonline.com, (accessed August 2011).
[2]. www.akosim.com, (accessed September 2011).
[3]. http://www.UMTSWorld.com/UMTS Network Capacity Planning.htm, (accessed October 2010).
[4]. http://www.UMTSWorld.com/UMTS Network Coverage Planning.htm, (accessed October 2010).
[5]. Agrawal D P, Zeng Q, “Introduction to Wireless and Mobile Systems”, Thomson Books / Cole,
Chapter 3, 2003.
[6]. Jensen, J. “R. Introductory Digital Image Processing: A Remote Sensing Perspective.”
Englewood Cliffs, New Jersey: Prentice-Hall. 1986
[7]. James R. Anderson, Ernest E. Hardy, and John T. Roach, Richard E. Witmer, ”A Land Use and
Land Cover Classification System for Use with Remote Sensor Data”, Geological Survey
Professional Paper 964, United States Government Printing Office, Washington: 1976
[8]. Hord, R. M. “Digital Image Processing of Remotely Sensed Data”. Academic Press, New York,
1982.
[9]. Khan AP, Porwal S,Rathi VS, “Computation of ideal location for 3G communication towers in
urban areas on web based 3D environment”,M4D2012,New Delhi .