Generalised-structure-tensor-based infrared small target detection

Gao, Ch.Q.; Tian, Jiangmin; Wang, P.

doi:10.1049/el:20081781

Cited by 47 publications

(15 citation statements)

References 4 publications

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“…In this study, to evaluate the effectiveness of the algorithm, the infrared images of the above six scenes are tested and compared with seven classical algorithms in the line, namely, the robust principal component analysis algorithm (RPCA), improved top-hat filtering algorithm (top-hat), improved various anisotropy filtering algorithm, generalized structure tensor algorithm (GST) [30] ,…”

Section: III Test Results and Analysismentioning

confidence: 99%

“…Moreover, to evaluate the detection effect of the algorithm, three evaluation metrics are introduced in this paper, structural similarity (SSIM) [33] , background suppression factor (BSF), and SNR. SSIM mainly evaluates the magnitude of the similarity between the background image predicted by the algorithm and the original image, which is calculated as follows (2 (30) where  in and  out are the mean squared differences between the input image and the difference image, respectively. The SNR can be used to evaluate the contrast between the target and the noise energy obtained by the algorithm and thus verify the detection effect of the algorithm, which is calculated as follows: the SNR is still larger in different sequences compared with other algorithms, which proves that the algorithm proposed in this paper has a greater advantage in target detection.…”

Section: III Test Results and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Fan

Chen

et al. 2022

IEEE Access

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In complex non-smooth backgrounds, infrared dim and small target targets generally have lower energy and occupy fewer pixels, and are easily swamped by clutter. To improve the detection capability of dim and small targets in non-smooth scenes, this paper proposes a new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model. The method first establishes a new gradient difference regularization to constrain the low-rank subspaces of different image components, then construct a gradient difference regularization-based principal component decomposition model (GDR-PCD), and finally decomposes the model using the overlapping directional multiplier method to obtain the background impedance. The experimental results show that the method performs better in all six sequential image scenes than the traditional algorithm. Furthermore, the detection results verify the algorithm's effectiveness in this paper.INDEX TERMS infrared dim and small target detection, principal component decomposition model, gradient difference regularization principal component decomposition model, overlapping directional multiplier method

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Section: III Test Results and Analysismentioning

confidence: 99%

Section: III Test Results and Analysismentioning

confidence: 99%

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Fan

Chen

et al. 2022

IEEE Access

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“…2) Baseline Methods: In order to highlight the detection performance of the proposed method, we compare it with other eight infrared dim small target detection methods, including two classical methods (MAXMED [5] and TOPHAT [19]) and six recently proposed methods (GST [9], IPIAPG [8], MSRLCM [15], MSAAGD [26], NRAM [44], and PSTNN [43]). The primary parameter settings of these methods are given in Table II.…”

Section: B Experimental Setup 1) Datasetmentioning

confidence: 99%

Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

Xue

Shao

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense-defense confrontation, etc. In this paper, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle (UAV) target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target and noise. Then, we put a nonconvex lowrank approximation on the background patch matrix to suppress the background edges, and put a reweighted l1,1 norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both l1,1 matrix norm and l2,1 matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis.

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“…We found many algorithms to detect the small target on the two data sets with low SCR, but some algorithms cannot play a role in these two data sets. Here, we choose eight effective algorithms for comparison: the algorithm based on local intensity and gradient properties (LIG) [49], the algorithm via non-convex rank approximation minimization joint l2 (NRAM) [50], the algorithm based on variance difference (Var_Diff) [51], the algorithm based on the multi-scale absolute average grey difference (AAGD) [51], the algorithm based on multi-scale Laplacian of Gaussian (LOG) [51], the algorithm based on the infrared patch-image model (IPI) [52], the algorithm based on density peaks searching and maximum-grey region growing (DPIR) [53], and the algorithm based on generalized-structure-tensor (GST) [54].…”

Section: Comparisonmentioning

confidence: 99%

CNN‐based infrared dim small target detection algorithm using target‐oriented shallow‐deep features and effective small anchor

Lü

et al. 2020

IET Image Processing

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For the extremely small size and low signal‐to‐clutter ratio, target detection in infrared images is still a considerable challenge. Specifically, it is very difficult to detect the point targets because there is no texture and shape information can be used. A target‐oriented shallow‐deep feature‐based detection algorithm is proposed, opening up a promising direction for convolutional neural network‐based infrared dim small target detection algorithms. To ensure that small target instances can be used correctly for networks, the effective small anchor is designed according to the shallow layer of ResNet50. To determine whether a detection result belongs to the target, the authors depend on whether the detection centre is included in the ground truth area, rather than on the Intersection Over Union overlap rate, which avoids misjudging the detection result. In this way, small targets can be trained and detected correctly through ResNet50. More importantly, the authors demonstrate that spatially finer shallow features are crucial for small target detection and that semantically stronger deep features are helpful for improving detection probability. Experimental results on simulation data sets and real data sets show that the proposed algorithm can detect the point target when the local signal‐to‐clutter ratio is approximately 1.3, displaying infinite advantage and great potentiality.

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Generalised-structure-tensor-based infrared small target detection

Cited by 47 publications

References 4 publications

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

CNN‐based infrared dim small target detection algorithm using target‐oriented shallow‐deep features and effective small anchor

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