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Li B, Xu Z, Zhang J, Zhao Q. Single-Pixel Background Modeling Algorithm for Strong Sky Scenes Based on Local Region Spatial Bases. SENSORS (BASEL, SWITZERLAND) 2023; 23:522. [PMID: 36617120 PMCID: PMC9824830 DOI: 10.3390/s23010522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/18/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
In the dim-small target detection field, background suppression is a key technique for stably extracting the target. In order to effectively suppress the background to enhance the target, this paper presents a novel background modeling algorithm, which constructs base functions for each pixel based on the local region background and models the background of each pixel, named single pixel background modeling (SPB). In SPB, the low-rank blocks of the local backgrounds are first obtained to construct the background base functions of the center pixel. Then, the background of the center pixel is optimally estimated by the background bases. Experiments demonstrate that in the case of extremely low signal-to-noise ratio (SNR < 1.5 dB) and complex motion state of targets, SPB can stably and effectively separate the target from the strongly undulant sky background. The difference image obtained via SPB background modeling has the characters: the non-target residual could be white noise, and the target is significantly enhanced. Compared with the other typical five algorithms, SPB remarkably outperforms other algorithms to detect the target of a low signal-to-noise ratio.
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Affiliation(s)
- Biao Li
- School of Computer Sciences and Engineering, Guilin University of Aerospace Technology, Jinji Road, Guilin 541004, China
| | - Zhiyong Xu
- Institute of Optics and Electronics, Chinese Academy of Sciences, Guangdian Avenue, Chengdu 610209, China
- University of Chinese Academy of Sciences, Yuquan Road, Beijing 100049, China
| | - Jianlin Zhang
- Institute of Optics and Electronics, Chinese Academy of Sciences, Guangdian Avenue, Chengdu 610209, China
- University of Chinese Academy of Sciences, Yuquan Road, Beijing 100049, China
| | - Quanyou Zhao
- School of Computer Sciences and Engineering, Guilin University of Aerospace Technology, Jinji Road, Guilin 541004, China
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Xi T, Yuan L, Sun Q. A Combined Approach to Infrared Small-Target Detection with the Alternating Direction Method of Multipliers and an Improved Top-Hat Transformation. SENSORS (BASEL, SWITZERLAND) 2022; 22:7327. [PMID: 36236434 PMCID: PMC9571038 DOI: 10.3390/s22197327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
In infrared small target detection, the infrared patch image (IPI)-model-based methods produce better results than other popular approaches (such as max-mean, top-hat, and human visual system) but in some extreme cases it suffers from long processing times and inconsistent performance. In order to overcome these issues, we propose a novel approach of dividing the traditional target detection process into two steps: suppression of background noise and elimination of clutter. The workflow consists of four steps: after importing the images, the second step applies the alternating direction multiplier method to preliminarily remove the background. Comparatively to the IPI model, this step does not require sliding patches, resulting in a significant reduction in processing time. To eliminate residual noise and clutter, the interim results from morphological filtering are then processed in step 3 through an improved new top-hat transformation, using a threefold structuring element. The final step is thresholding segmentation, which uses an adaptive threshold algorithm. Compared with IPI and the new top-hat methods, as well as some other widely used methods, our approach was able to detect infrared targets more efficiently (90% less computational time) and consistently (no sudden performance drop).
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Affiliation(s)
- Tengyan Xi
- Key Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hang Kong University, Nanchang 330031, China
| | - Lihua Yuan
- Key Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hang Kong University, Nanchang 330031, China
| | - Quanbin Sun
- School of Computing and Digital Technology, Birmingham City University, Birmingham B5 5JU, UK
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Abstract
Infrared small target detection under intricate background and heavy noise is one of the crucial tasks in the field of remote sensing. Conventional algorithms can fail in detecting small targets due to the low signal-to-noise ratios of the images. To solve this problem, an effective infrared small target detection algorithm inspired by random walks is presented in this paper. The novelty of our contribution involves the combination of the local contrast feature and the global uniqueness of the small targets. Firstly, the original pixel-wise image is transformed into an multi-dimensional image with respect to the local contrast measure. Secondly, a reconstructed seeds selection map (SSM) is generated based on the multi-dimensional image. Then, an adaptive seeds selection method is proposed to automatically select the foreground seeds potentially placed in the areas of the small targets in the SSM. After that, a confidence map is constructed using a modified random walks (MRW) algorithm to represent the global uniqueness of the small targets. Finally, we segment the targets from the confidence map by utilizing an adaptive threshold. Extensive experimental evaluation results on a real test dataset demonstrate that our algorithm is superior to the state-of-the-art algorithms in both target enhancement and detection performance.
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Wang Y, Xu X, Yue N, Chen J. Small target detection using edge-preserving background estimation based on maximum patch similarity. INT J ADV ROBOT SYST 2017. [DOI: 10.1177/1729881417744822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
- Yuehuan Wang
- School of Automation, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
- National Key Laboratory of Science and Technology on Multispectral Information Processing, Wuhan, People’s Republic of China
| | - Xueping Xu
- School of Automation, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Nuoning Yue
- School of Automation, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Jie Chen
- The 9th Designing of China Aerospace Science Industry Corp, Wuhan, People’s Republic of China
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Robust Small Target Co-Detection from Airborne Infrared Image Sequences. SENSORS 2017; 17:s17102242. [PMID: 28961206 PMCID: PMC5677333 DOI: 10.3390/s17102242] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Revised: 09/17/2017] [Accepted: 09/25/2017] [Indexed: 11/17/2022]
Abstract
In this paper, a novel infrared target co-detection model combining the self-correlation features of backgrounds and the commonality features of targets in the spatio-temporal domain is proposed to detect small targets in a sequence of infrared images with complex backgrounds. Firstly, a dense target extraction model based on nonlinear weights is proposed, which can better suppress background of images and enhance small targets than weights of singular values. Secondly, a sparse target extraction model based on entry-wise weighted robust principal component analysis is proposed. The entry-wise weight adaptively incorporates structural prior in terms of local weighted entropy, thus, it can extract real targets accurately and suppress background clutters efficiently. Finally, the commonality of targets in the spatio-temporal domain are used to construct target refinement model for false alarms suppression and target confirmation. Since real targets could appear in both of the dense and sparse reconstruction maps of a single frame, and form trajectories after tracklet association of consecutive frames, the location correlation of the dense and sparse reconstruction maps for a single frame and tracklet association of the location correlation maps for successive frames have strong ability to discriminate between small targets and background clutters. Experimental results demonstrate that the proposed small target co-detection method can not only suppress background clutters effectively, but also detect targets accurately even if with target-like interference.
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Wei Y, Lu Z, Yuan G, Fang Z, Huang Y. Sparsity Adaptive Matching Pursuit Detection Algorithm Based on Compressed Sensing for Radar Signals. SENSORS 2017; 17:s17051120. [PMID: 28505085 PMCID: PMC5470796 DOI: 10.3390/s17051120] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2017] [Revised: 05/04/2017] [Accepted: 05/11/2017] [Indexed: 11/16/2022]
Abstract
In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem, a novel sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed. This algorithm executes the detection task by updating the support set and gradually increasing the sparsity to approximate the original signal. To verify the effectiveness of the proposed algorithm, the data collected in 2010 at Pingtan, which located on the coast of the East China Sea, were applied. Experiment results illustrate that the proposed method adaptively completes the detection task without knowing the signal sparsity, and the similar detection performance is close to the matching pursuit (MP) and orthogonal matching pursuit (OMP) detection algorithms.
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Affiliation(s)
- Yanbo Wei
- College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China.
| | - Zhizhong Lu
- College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China.
| | - Gannan Yuan
- College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China.
| | - Zhao Fang
- College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China.
| | - Yu Huang
- College of Science, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China.
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Li Y, Zhang Y, Yu JG, Tan Y, Tian J, Ma J. A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2016.07.042] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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8
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Wu Y, Lu H, Zhao F, Zhang Z. Estimating Shape and Micro-Motion Parameter of Rotationally Symmetric Space Objects from the Infrared Signature. SENSORS 2016; 16:s16101722. [PMID: 27763500 PMCID: PMC5087509 DOI: 10.3390/s16101722] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 10/05/2016] [Accepted: 10/11/2016] [Indexed: 12/04/2022]
Abstract
Shape serves as an important additional feature for space target classification, which is complementary to those made available. Since different shapes lead to different projection functions, the projection property can be regarded as one kind of shape feature. In this work, the problem of estimating the projection function from the infrared signature of the object is addressed. We show that the projection function of any rotationally symmetric object can be approximately represented as a linear combination of some base functions. Based on this fact, the signal model of the emissivity-area product sequence is constructed, which is a particular mathematical function of the linear coefficients and micro-motion parameters. Then, the least square estimator is proposed to estimate the projection function and micro-motion parameters jointly. Experiments validate the effectiveness of the proposed method.
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Affiliation(s)
- Yabei Wu
- Automatic Target Recognition Laboratory, National University of Defense Technology, Deya Road, Changsha 410073, China.
| | - Huanzhang Lu
- Automatic Target Recognition Laboratory, National University of Defense Technology, Deya Road, Changsha 410073, China.
| | - Fei Zhao
- Automatic Target Recognition Laboratory, National University of Defense Technology, Deya Road, Changsha 410073, China.
| | - Zhiyong Zhang
- Automatic Target Recognition Laboratory, National University of Defense Technology, Deya Road, Changsha 410073, China.
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Joint infrared target recognition and segmentation using a shape manifold-aware level set. SENSORS 2015; 15:10118-45. [PMID: 25938202 PMCID: PMC4481947 DOI: 10.3390/s150510118] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2015] [Revised: 04/17/2015] [Accepted: 04/22/2015] [Indexed: 11/17/2022]
Abstract
We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation).
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Sanna A, Lamberti F. Advances in target detection and tracking in Forward-Looking InfraRed (FLIR) imagery. SENSORS 2014; 14:20297-303. [PMID: 25353980 PMCID: PMC4279483 DOI: 10.3390/s141120297] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/23/2014] [Accepted: 10/23/2014] [Indexed: 11/16/2022]
Abstract
Here we give context to the Special Issue on “Detection and Tracking of Targets in Forward-Looking InfraRed (FLIR) Imagery” in Sensors. We start with an introduction to the role of infrared images in today's vision-based applications, by outlining the specific challenges that characterize detection and tracking in FLIR images. We then illustrate why selected papers have been chosen to represent the domain of interest, by summarizing their main contributions to the state-of-the-art. Lastly, we sum up the main evidence found, and we underline some of the aspects that are worthy of further investigation in future research activities.
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Affiliation(s)
- Andrea Sanna
- Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24, Torino 10129, Italy.
| | - Fabrizio Lamberti
- Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24, Torino 10129, Italy.
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