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A Comprehensive Benchmark for Spatiotemporal Tensor-Based Infrared Small Target Detection

Fengyi Wu,Siyu Chen,Simin Liu,Bingjie Tao,Junhai Luo,Zhenming Peng

Published 2026 in IEEE Transactions on Geoscience and Remote Sensing

ABSTRACT

Optimization-based methods have gained prominence in infrared small target detection (ISTD) due to their effectiveness in handling sparse targets. However, existing approaches often struggle with spatiotemporal modeling, resulting in suboptimal background suppression and target misclassification. Moreover, stringent recovery tolerances limit computational efficiency in sequential infrared processing. To overcome these challenges, we introduce a novel approach that enhances both background estimation and target detection by leveraging spatiotemporal features. Specifically, we integrate a 4-D spatiotemporal tensor with a fully connected tensor network (FCTN) completion strategy, utilizing the sum of nuclear norms to maximize intermode correlations while minimizing clutter. For target extraction, we employ a dual Gaussian-core spatial saliency filter (SSF) and a temporal difference filter (TDF), enabling precise discrimination between true targets and static interference. The resulting model—spatial–temporal prior-assisted FCTN (STPA-FCTN)—balances background suppression with target preservation. Furthermore, we systematically analyze recovery convergence in STPA-FCTN and identify an optimal tolerance of $10^{-3}$ using the proposed open-source TensorISTD benchmark. Extensive evaluations on public datasets confirm the superior numerical and visual performance of our method over state-of-the-art techniques. Code and benchmark resources are available at https://github.com/fengyiwu98/TensorISTD

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