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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 .