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IJMLC 2011 Vol.1(5): 427-434 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.64

Ground Targets Recognition from Flying Vehicles using Camera/SAR Imaging Systems

Alaa El-Din Sayed Hafez, Wiedo Hu, and Ahmed Mohamed Gharyb

Abstract—This paper proposes a ground targets recognition system for flying vehicles using Camera/Synthetic Aperture Radar (SAR) imaging systems. The proposed method is an image processing technique to improve the precision of the INS for detecting and tracking the ground objects from flying vehicles. Template matching is one of the methods used for ground object detection and tracking. Synthetic Aperture Radar (SAR) is also used as ground object detection with ATR technique. Robust and reliable object detection is a critical step of object recognition. Our focus is on flying systems equipped with camera and SAR to capture photos for the ground and recognize it. SAR is a type of imaging radar in which the relative movement of the antenna with respect to the target is utilized. Through the simultaneous processing of the radar reflections over the movement of the antenna via the Range Doppler Algorithm (RDA). The proposed method is independent on the altitude or the orientation of the object. The algorithm is simulated using Matlab program and the numerical experiments are shown which verify the object detection for a wide range altitude and orientation. The results show superiority of this method for identifying and recognizing the ground objects.

Index Terms—INS. Template Match, SAR, ATR

AAlaa El-Din Sayed Hafez is with Electrical engineering Department, Alexandria University, Alexandria, Egypt (email: alaahafez@ ieee.org).
Wiedo Hu is with Beijing University of Aeronautics & Astronautics, China (e-mail: weiduo.hu@gmail.com). Ahmed Mohamed Gharuib is with Beijing University of Aeronautics & Astronautics, Egypt (email: ahmedgharyb@gmail.com).

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Cite: Alaa El-Din Sayed Hafez, Wiedo Hu, and Ahmed Mohamed Gharyb, "Ground Targets Recognition from Flying Vehicles using Camera/SAR Imaging Systems," International Journal of Machine Learning and Computing vol. 1, no. 5, pp. 427-434, 2011.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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