Infrared Target-Background Separation Based on Weighted Nuclear Norm Minimization and Robust Principal Component Analysis

Sur Singh Rawat (), Sukhendra Singh, Youseef Alotaibi, Saleh Alghamdi and Gyanendra Kumar ()
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Sur Singh Rawat: Department of Computer Science and Engineering, JSS Academy of Technical Education, Noida 201301, India
Sukhendra Singh: Department of Information Technology, JSS Academy of Technical Education, Noida 201301, India
Youseef Alotaibi: Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia
Saleh Alghamdi: Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
Gyanendra Kumar: School of Computing Sciences and Engineering, Galgotias University, Greater Noida 201310, India

Mathematics, 2022, vol. 10, issue 16, 1-22

Abstract: The target detection ability of an infrared small target detection (ISTD) system is advantageous in many applications. The highly varied nature of the background image and small target characteristics make the detection process extremely difficult. To address this issue, this study proposes an infrared patch model system using non-convex (IPNCWNNM) weighted nuclear norm minimization (WNNM) and robust principal component analysis (RPCA). As observed in the most advanced methods of infrared patch images (IPI), the edges, sometimes in a crowded background, can be detected as targets due to the extreme shrinking of singular values (SV). Therefore, a non-convex WNNM and RPCA have been utilized in this paper, where varying weights are assigned to the SV rather than the same weights for all SV in the existing nuclear norm minimization (NNM) of IPI-based methods. The alternate direction method of multiplier (ADMM) is also employed in the mathematical evaluation of the proposed work. The observed evaluations demonstrated that in terms of background suppression and target detection proficiency, the suggested technique performed better than the cited baseline methods.

Keywords: infrared search (IRST) and track system; RPCA; NNM; IPI; signal to clutter ratio (SCR); SCR gain (SCRG) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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