Target detection of ISAR data by principal component transform on co-occurrence matrix

  • Authors:

  • Mousumi Gupta;Debasish Bhaskar;Rabindranath Bera;Sambhunath Biswas

  • Affiliations:

  • Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Majitar, Rangpo, Sikkim 737132, India;Department of Electronics and Communication, Sikkim Manipal Institute of Technology, Majitar, Rangpo, Sikkim 737132, India;Department of Electronics and Communication, Sikkim Manipal Institute of Technology, Majitar, Rangpo, Sikkim 737132, India;Machine Intelligence Unit, Indian Statiscal Institute, 203 Barrackpore Trunk Road, Kolkata 700035, India

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2012

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Abstract

Issue of automated target detection in ISAR can be stated as what features enhance objects of interest from the rest of the data. Much experimentation done in this area have used Fourier transforms for preprocessing the raw signal data. Generally the ISAR data are comes with a matrix of complex number values and therefore intuitive logic appears to favor a Fourier transform. A hypothesis was made that a Fourier transform in preprocessing may mask some data that could be part of feature used to threshold the object from background. Thus a trial was done on MATLAB simulated ISAR data to see if such data can be transformed into a matrix to visualize objects by preprocessing with principle component transform followed by some modification conventional thresholding techniques i.e. gray level co-occurrence matrix. Since it would be difficult to do so in complex valued matrices, these matrices had been decomposed to real valued and the imaginary valued matrices separately. Advantages of simulated data were that variables could be defined and changes in preprocessing transform and thresholding result could be compared with significant accuracy before a trial with actual performance of ISAR imagery. The preliminary result in this paper does show that preprocessing transform need not be Fourier. Principle component transform may bring about features that enhance thresholding values for Automatic target detection. Thresholding in conventional methods is done by finding a fixed value to create a binary image highlighting the object. In the modification proposed here single value thresholding objects and then spatially locating the object in a binary matrix may circumvented.