Robust Infrared Maritime Target Detection Based on Visual Attention and Spatiotemporal Filtering

Dong, Lili; Wang, Bin; Zhao, Ming; Xu, Wenhai

doi:10.1109/tgrs.2017.2660879

Cited by 88 publications

(36 citation statements)

References 30 publications

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“…Optimization problem (18) can also be solved by using the soft thresholding operator. (18) can be solved by two equations:…”

Section: Solution Of the Proposed Modelmentioning

confidence: 99%

“…A large number of methods have been developed to address the issues of small target detection. ese methods can be roughly classified into two categories: single-frame detection [5][6][7][8][9][10][11][12][13][14][15][16] and sequential multiframe detection [17][18][19]. Recently, Gao et al [17] employed the mixture of the Gaussians model [20] with the Markov random field to model the complex noise of which the target is assumed as a component.…”

Section: Introductionmentioning

confidence: 99%

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Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Fang

Chen

Liu

et al. 2020

Mathematical Problems in Engineering

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Infrared small target detection plays an important role in infrared search and tracking systems applications. It is difficult to perform target detection when only a single image with complex background clutters and noise is available, where the key is to suppress the complex background clutters and noise while enhancing the small target. In this paper, we propose a novel model for separating the background from the small target based on nonlocal self-similarity for infrared patch-image. A total variation-based regularization term for the small target image is incorporated into the model to suppress the residual background clutters and noise while enhancing the smoothness of the solution. Furthermore, a reweighted sparse constraint is imposed for the small target image to remove the nontarget points while better highlighting the small target. For higher computational efficiency, an adapted version of the alternating direction method of multipliers is employed to solve the resulting minimization problem. Comparative experiments with synthetic and real data demonstrate that the proposed method is superior in detection performance to the state-of-the-art methods in terms of both objective measure and visual quality.

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“…Optimization problem (18) can also be solved by using the soft thresholding operator. (18) can be solved by two equations:…”

Section: Solution Of the Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Fang

Chen

Liu

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Many approaches have been reported for addressing these issues, which roughly include two classes of mainstream detection methods: sequential detection [5,6] and single-frame detection [7,8]. Traditional sequential detection methods are driven by prior information such as target trajectory, velocity and shape, and essentially utilize the adjacent inter-frame knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…Representative multiple targets images from the datasets and the separated target images obtained by six low-rank recovery-based methods (5)(6)(7)(8). are four representative multiple targets images from the tested datasets.…”

mentioning

confidence: 99%

Detection of Small Target Using Schatten 1/2 Quasi-Norm Regularization with Reweighted Sparse Enhancement in Complex Infrared Scenes

Zhou

Dai

et al. 2019

Remote Sensing

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In uniform infrared scenes with single sparse high-contrast small targets, most existing small target detection algorithms perform well. However, when encountering multiple and/or structurally sparse targets in complex backgrounds, these methods potentially lead to high missing and false alarm rate. In this paper, a novel and robust infrared single-frame small target detection is proposed via an effective integration of Schatten 1/2 quasi-norm regularization and reweighted sparse enhancement (RS1/2NIPI). Initially, to achieve a tighter approximation to the original low-rank regularized assumption, a nonconvex low-rank regularizer termed as Schatten 1/2 quasi-norm (S1/2N) is utilized to replace the traditional convex-relaxed nuclear norm. Then, a reweighted l1 norm with adaptive penalty serving as sparse enhancement strategy is employed in our model for suppressing non-target residuals. Finally, the small target detection task is reformulated as a problem of nonconvex low-rank matrix recovery with sparse reweighting. The resulted model falls into the workable scope of inexact augment Lagrangian algorithm, in which the S1/2N minimization subproblem can be efficiently solved by the designed softening half-thresholding operator. Extensive experimental results on several real infrared scene datasets validate the superiority of the proposed method over the state-of-the-arts with respect to background interference suppression and target extraction.

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“…Of which, detection algorithms based on background modeling mainly are composed of traditional detection algorithms based on time domain [2], space domain [3,4] or frequency domain filtering [5] and background modeling methods based on statistical characteristics [6,7], etc. Detection algorithms based on machine learning mainly include detection algorithms based on visual saliency [8,9], detection algorithms based on dictionary learning and sparse representation [10,11], detection algorithms based on low-rank background [12,13], and detection algorithms based on based on CNN (Convolutional Neural Network) [14][15][16][17], etc. When the target is in a low SNR scene, using merely the single-frame information to detect dim-small targets in the above singleframe filtering detection algorithm may result in a high false alarm rate in the detection results.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

Fan

2019

International Journal of Optics

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In order to improve the detection ability of dim and small targets in dynamic scenes, this paper first proposes an anisotropic gradient background modeling method combined with spatial and temporal information and then uses the multidirectional gradient maximum of neighborhood blocks to segment the difference maps. On the basis of previous background modeling and segmentation extraction candidate targets, a dim small target detection algorithm for local energy aggregation degree of sequence images is proposed. Experiments show that compared with the traditional algorithm, this method can eliminate the interference of noise to the target and improve the detection ability of the system effectively.

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Robust Infrared Maritime Target Detection Based on Visual Attention and Spatiotemporal Filtering

Cited by 88 publications

References 30 publications

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Detection of Small Target Using Schatten 1/2 Quasi-Norm Regularization with Reweighted Sparse Enhancement in Complex Infrared Scenes

Dim and Small Target Detection Based on Local Energy Aggregation Degree of Sequence Images

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