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A novel neural network based image reconstruction model with scale and rotation invariance for target identification and classification for Active millimetre wave imaging

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Abstract

Millimetre wave imaging (MMW) is gaining tremendous interest among researchers, which has potential applications for security check, standoff personal screening, automotive collision-avoidance, and lot more. Current state-of-art imaging techniques viz. microwave and X-ray imaging suffers from lower resolution and harmful ionizing radiation, respectively. In contrast, MMW imaging operates at lower power and is non-ionizing, hence, medically safe. Despite these favourable attributes, MMW imaging encounters various challenges as; still it is very less explored area and lacks suitable imaging methodology for extracting complete target information. Keeping in view of these challenges, a MMW active imaging radar system at 60 GHz was designed for standoff imaging application. A C-scan (horizontal and vertical scanning) methodology was developed that provides cross-range resolution of 8.59 mm. The paper further details a suitable target identification and classification methodology. For identification of regular shape targets: mean-standard deviation based segmentation technique was formulated and further validated using a different target shape. For classification: probability density function based target material discrimination methodology was proposed and further validated on different dataset. Lastly, a novel artificial neural network based scale and rotation invariant, image reconstruction methodology has been proposed to counter the distortions in the image caused due to noise, rotation or scale variations. The designed neural network once trained with sample images, automatically takes care of these deformations and successfully reconstructs the corrected image for the test targets. Techniques developed in this paper are tested and validated using four different regular shapes viz. rectangle, square, triangle and circle.

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Acknowledgment

Authors would like to thank Department of Science and Technology (DST), India for their financial assistance in carrying out this research work under project grant no. SR/WOS-A/ET61/2011.

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Authors and Affiliations

  1. Centre of Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, India

    Smriti Agarwal & Dharmendra Singh

  2. Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Smriti Agarwal, Amit Singh Bisht, Dharmendra Singh & Nagendra Prasad Pathak

Authors
  1. Smriti Agarwal
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  2. Amit Singh Bisht
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  3. Dharmendra Singh
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  4. Nagendra Prasad Pathak
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Corresponding author

Correspondence to Dharmendra Singh.

Appendix

Appendix

Table 9 Complete detailed list of different target samples taken with varying sizes and orientations
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Agarwal, S., Bisht, A.S., Singh, D. et al. A novel neural network based image reconstruction model with scale and rotation invariance for target identification and classification for Active millimetre wave imaging. J Infrared Milli Terahz Waves 35, 1045–1067 (2014). https://doi.org/10.1007/s10762-014-0109-5

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  • DOI: https://doi.org/10.1007/s10762-014-0109-5

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