Feasibility study of mobile tower locations using classification
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  • 1. 1/8 Feasibility study of mobile tower locations using classification result of satellite image Ajaze Parvez Khan
  • 2. 2/8 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……
  • 3. 3/8 Cellular Network Planning
  • 4. 4/8 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. 5
  • 6. 6/8  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
  • 7. 7/8 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 )] 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 class X = the measurement vector of the candidate pixel Mc = 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 function T = transposition function 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
  • 8. Dialog for 3D Controls Dialog for inputting 3G parameters TrueColor Image Draped over the DEM Study Area Results 8/8
  • 9. Results 9/8 T O W E R S Position Installation Status/Solution Results A Completely sits over Built-Up area Installation Possible B Completely sits over forest area REJECTED C Sits over mixed classes/signature area Can be installed (If land or building for installation is available, field visit required)
  • 10. 10/8 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.
  • 11. 11/8 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 .