Neural Spatial-Temporal Tensor Representation for Infrared Small Target Detection

• URL: http://arxiv.org/abs/2412.17302v1
• Date: Mon, 23 Dec 2024 05:46:08 GMT
• Title: Neural Spatial-Temporal Tensor Representation for Infrared Small Target Detection
• Authors: Fengyi Wu, Simin Liu, Haoan Wang, Bingjie Tao, Junhai Luo, Zhenming Peng,
• Abstract summary: We introduce a Neural-represented spatial-temporal model (NeurSTT) for infrared small target detection. NeurSTT enhances spatial-temporal correlations in background approximation, thereby supporting target detection in an unsupervised manner. Visual and numerical results across various datasets demonstrate that our method outperforms the suboptimal method on $256 times 256$ sequences.
• Score: 3.7038542578642724
• License:
• Abstract: Optimization-based approaches dominate infrared small target detection as they leverage infrared imagery's intrinsic low-rankness and sparsity. While effective for single-frame images, they struggle with dynamic changes in multi-frame scenarios as traditional spatial-temporal representations often fail to adapt. To address these challenges, we introduce a Neural-represented Spatial-Temporal Tensor (NeurSTT) model. This framework employs nonlinear networks to enhance spatial-temporal feature correlations in background approximation, thereby supporting target detection in an unsupervised manner. Specifically, we employ neural layers to approximate sequential backgrounds within a low-rank informed deep scheme. A neural three-dimensional total variation is developed to refine background smoothness while reducing static target-like clusters in sequences. Traditional sparsity constraints are incorporated into the loss functions to preserve potential targets. By replacing complex solvers with a deep updating strategy, NeurSTT simplifies the optimization process in a domain-awareness way. Visual and numerical results across various datasets demonstrate that our method outperforms detection challenges. Notably, it has 16.6$\times$ fewer parameters and averaged 19.19\% higher in $IoU$ compared to the suboptimal method on $256 \times 256$ sequences.

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