Small Infrared Target Detection Based on Low-Rank and Sparse Matrix Decomposition

2012 ◽  
Vol 239-240 ◽  
pp. 214-218 ◽  
Author(s):  
Cheng Yong Zheng ◽  
Hong Li
Keyword(s):  
Target Detection ◽  
Sparse Matrix ◽  
Infrared Image ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Target Component ◽  
Lagrange Method ◽  
Rank Matrix ◽  
Augmented Lagrange Method ◽  
Low Rank Matrix

Sparse and low-rank matrix decomposition (SLMD) tries to decompose a matrix into a low-rank matrix and a sparse matrix, it has recently attached much research interest and has good applications in many fields. An infrared image with small target usually has slowly transitional background, it can be seen as the sum of low-rank background component and sparse target component. So by SLMD, the sparse target component can be separated from the infrared image and then be used for small infrared target detection (SITD). The augmented Lagrange method, which is currently the most efficient algorithm used for solving SLMD, was applied in this paper for SITD, some parameters were analyzed and adjusted for SITD. Experimental results show our algorithm is fast and reliable.

scholarly journals Flying Small Target Detection for Anti-UAV Based on a Gaussian Mixture Model in a Compressive Sensing Domain

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2168 ◽  
Author(s):  
Chuanyun Wang ◽  
Tian Wang ◽  
Ershen Wang ◽  
Enyan Sun ◽  
Zhen Luo
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Sparse Matrix ◽  
Infrared Image ◽  
Matrix Decomposition ◽  
Gaussian Mixture ◽  
Visual Surveillance ◽  
Low Rank ◽  
Small Target ◽  
Small Target Detection

Addressing the problems of visual surveillance for anti-UAV, a new flying small target detection method is proposed based on Gaussian mixture background modeling in a compressive sensing domain and low-rank and sparse matrix decomposition of local image. First of all, images captured by stationary visual sensors are broken into patches and the candidate patches which perhaps contain targets are identified by using a Gaussian mixture background model in a compressive sensing domain. Subsequently, the candidate patches within a finite time period are separated into background images and target images by low-rank and sparse matrix decomposition. Finally, flying small target detection is achieved over separated target images by threshold segmentation. The experiment results using visible and infrared image sequences of flying UAV demonstrate that the proposed methods have effective detection performance and outperform the baseline methods in precision and recall evaluation.

Clutter suppression of infrared image based on three-component low-rank matrix decomposition

2015 ◽  
Vol 23 (7) ◽  
pp. 2069-2078 ◽  
Author(s):  
何玉杰 HE Yu-jie ◽  
李敏 LI Min ◽  
张金利 ZHANG Jin-li ◽  
姚俊萍 YAO Jun-ping
Keyword(s):  
Infrared Image ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Clutter Suppression ◽  
Rank Matrix ◽  
Low Rank Matrix

scholarly journals GPR Target Detection by Joint Sparse and Low-Rank Matrix Decomposition

2019 ◽  
Vol 57 (5) ◽  
pp. 2583-2595 ◽  
Author(s):  
Fok Hing Chi Tivive ◽  
Abdesselam Bouzerdoum ◽  
Canicious Abeynayake
Keyword(s):  
Target Detection ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix

scholarly journals Single-Channel Speech Enhancement Based on Adaptive Low-Rank Matrix Decomposition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 37066-37076
Author(s):  
Chao Li ◽  
Ting Jiang ◽  
Sheng Wu ◽  
Jianxiao Xie
Keyword(s):  
Speech Enhancement ◽  
Single Channel ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix
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