An overview of marine moving target detection via high-resolution sparse representation

2016 ◽  
Author(s):  
Xiaohan Yu ◽  
Xiaolong Chen ◽  
Wenchao Hu ◽  
Jian Guan
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Target Detection ◽  
Moving Target ◽  
Moving Target Detection

Dim moving target detection algorithm based on spatio-temporal classification sparse representation

2014 ◽  
Vol 67 ◽  
pp. 273-282 ◽  
Author(s):  
Zhengzhou Li ◽  
Zhen Dai ◽  
Hongxia Fu ◽  
Qian Hou ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Spatio Temporal

scholarly journals Infrared small moving target detection using sparse representation-based image decomposition

2016 ◽  
Vol 76 ◽  
pp. 148-156 ◽  
Author(s):  
Hanlin Qin ◽  
Jiaojiao Han ◽  
Xiang Yan ◽  
Qingjie Zeng ◽  
Huixin Zhou ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Image Decomposition ◽  
Moving Target ◽  
Moving Target Detection

scholarly journals Correction to: Low noise moving target detection in high resolution radar using binary codes

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Majid Alotaibi
Keyword(s):  
High Resolution ◽  
Target Detection ◽  
Low Noise ◽  
Binary Codes ◽  
Moving Target ◽  
Moving Target Detection ◽  
High Resolution Radar

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals A Constrained Sparse-Representation-Based Spatio-Temporal Anomaly Detector for Moving Targets in Hyperspectral Imagery Sequences

2020 ◽  
Vol 12 (17) ◽  
pp. 2783
Author(s):  
Zhaoxu Li ◽  
Qiang Ling ◽  
Jing Wu ◽  
Zhengyan Wang ◽  
Zaiping Lin
Keyword(s):  
Anomaly Detection ◽  
Sparse Representation ◽  
Target Detection ◽  
Hyperspectral Imagery ◽  
Spectral Information ◽  
Moving Target ◽  
Complex Motion ◽  
Multiple Targets ◽  
Moving Target Detection ◽  
Spatio Temporal

At present, small dim moving target detection in hyperspectral imagery sequences is mainly based on anomaly detection (AD). However, most conventional detection algorithms only utilize the spatial spectral information and rarely employ the temporal spectral information. Besides, multiple targets in complex motion situations, such as multiple targets at different velocities and dense targets on the same trajectory, are still challenges for moving target detection. To address these problems, we propose a novel constrained sparse representation-based spatio-temporal anomaly detection algorithm that extends AD from the spatial domain to the spatio-temporal domain. Our algorithm includes a spatial detector and a temporal detector, which play different roles in moving target detection. The former can suppress moving background regions, and the latter can suppress non-homogeneous background and stationary objects. Two temporal background purification procedures maintain the effectiveness of the temporal detector for multiple targets in complex motion situations. Moreover, the smoothing and fusion of the spatial and temporal detection maps can adequately suppress background clutter and false alarms on the maps. Experiments conducted on a real dataset and a synthetic dataset show that the proposed algorithm can accurately detect multiple targets with different velocities and dense targets with the same trajectory and outperforms other state-of-the-art algorithms in high-noise scenarios.

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