A Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes

Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse deco...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (17), p.4316
Main Authors: Aliha, Aersi, Liu, Yuhan, Ma, Yapeng, Hu, Yuxin, Pan, Zongxu, Zhou, Guangyao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Approximation
Background noise
complex scenes
Deep learning
Efficiency
Eigenvalues
False alarms
Hypothesis testing
image block matching
infrared small moving target detection
local prior weights
Matching
Mathematical analysis
Methods
Moving targets
Neural networks
Qualitative analysis
Remote sensing
Sparsity
Target detection
Tensors
Unmanned aerial vehicles
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Summary:Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). Qualitative and quantitative experimental analysis in various scenes demonstrates the superior detection performance and efficiency of the proposed algorithm for detecting infrared small moving targets in complex scenes.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15174316