STTM-SFR: Spatial-Temporal Tensor Modeling with Saliency Filter Regularization for Infrared Small Target Detection

Dongdong Pang, Pengge Ma, Tao Shan*, Wei Li, Ran Tao, Yueran Ma, Tianrun Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

Abstract

Detecting small infrared (IR) targets against low-Altitude complex background is always a challenge for IR search and tracking (IRST) system due to limited small target characteristics, the moving background caused by camera motion, and extremely cluttered backgrounds. The existing methods usually cause high false alarm or do not work against the chaotic low-Altitude complex background. In this article, a novel spatial-Temporal tensor model with saliency filter regularization (STTM-SFR) is developed to detect small IR targets. First, the small target detection task is transformed into a sparse and low-rank tensor optimization problem using the spatial-Temporal prior knowledge of background and target. The construction of the holistic STTM can retain the complete spatial-Temporal information of the original IR image sequence. Then, the SFR term limited between background and foreground aims to promote target saliency learning. That is to say, the SFR term can avoid the offset approximation of the low-rank tensor, so as to recover a clean target image from the original IR tensor. Finally, an effective alternating direction method of multipliers (ADMM) algorithm framework is designed to solve the proposed STTM-SFR model. The effectiveness and robustness of the STTM-SFR model are verified in six real IR scenes. Experimental results show that our method outperforms other baseline methods. Moreover, the proposed STTM-SFR method is more robust than the existing state-of-The-Art STTMs against low-Altitude moving backgrounds.

Original languageEnglish
Article number5623418
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume60
DOIs
Publication statusPublished - 2022

Keywords

  • Alternating direction method of multipliers (ADMM)
  • image sequence
  • low-Altitude moving background
  • saliency filter regularization (SFR)
  • small infrared (IR) target detection
  • spatialâÂÄa"temporal tensor model (STTM)

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