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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 18
• Main Authors: Pang, Dongdong, Ma, Pengge, Shan, Tao, Li, Wei, Tao, Ran, Ma, Yueran, Wang, Tianrun
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alternating direction method of multipliers (ADMM); Altitude; Analytical models; Approximation; Data models; Detection; False alarms; Image edge detection; image sequence; Infrared filters; Infrared imaging; Low altitude; low-altitude moving background; Mathematical analysis; Matrix decomposition; Methods; Object detection; Optimization; Regularization; Salience; saliency filter regularization (SFR); small infrared (IR) target detection; spatial–temporal tensor model (STTM); Target detection; Tensors; Tracking
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3172745

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.