Infrared Small Target Detection Based on Patch Image Model with Local and Global Analysis

Wang, Huan; Yang, Fei; Zhang, Congcong; Ren, Mengye

doi:10.1142/s021946781850002x

Cited by 17 publications

(14 citation statements)

References 16 publications

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“…First, the objective function is constructed according to characteristics of the tensor's background and target. Then, the objective function is solved and the detection results are obtained [23], [30], [32], [44], confirming that the combination of a tensor model and robust principal component analysis model can achieve good results. Based on small sample remote sensing data, this paper proposes a method based on small sample data [45], which uses visual features and sparse and low-rank decomposition to detect cirrus.…”

Section: A Related Workmentioning

confidence: 70%

“…Due to the improvement in optimization methods, the accuracy of infrared small target detection is constantly improving [30]- [38]. In optimization methods, principal component analysis (PCA) is a classic model [39], which reduces the dimensionality of high-dimensional data, removes sparse irrelevant information, and obtains the main information.…”

Section: A Related Workmentioning

confidence: 99%

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Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

et al. 2020

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Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this paper, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the 1 L norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimizationbased methods, this method has better detection performance and accuracy.

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Section: A Related Workmentioning

confidence: 70%

Section: A Related Workmentioning

confidence: 99%

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

et al. 2020

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“…Hence, structure tensor tends to give lower values at corners even if some of them are part of the edges sometimes. As pointed out in [62], when the weight stretching parameter h decreases, the difference would be more significant, causing an increase in the false alarm rate. In summary, on one hand, to preserve the target and prevent it from being completely lost, a smaller h is needed; in contrast, to avoid the interference of residuals, a larger h is needed.…”

Section: Local Prior Analysismentioning

confidence: 94%

“…where c is a nonnegative constant, ε > 0 is a small number to avoid division by zero, and k+1 denotes the (k+1)-th iteration. In some cases, c is fixed to 1 [42,62]. We combined the two weights to get a simplified form…”

Section: Model Constructionmentioning

confidence: 99%

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

2019

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Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.

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“…At the same time, the complex noise in an actual scene may also be considered a sparse component by the IPI model, which produces numerous false positives. To solve this issue, Wang et al [28] proposed a patch image model with local and global analysis (PILGA) to constrain the sparsity of noise patch images. However, the performance of these methods degrades rapidly while considering more heterogeneous backgrounds.…”

Section: Introductionmentioning

confidence: 99%

TNLRS: Target-Aware Non-Local Low-Rank Modeling With Saliency Filtering Regularization for Infrared Small Target Detection

Zhu

Liu

et al. 2020

IEEE Trans. on Image Process.

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Recently, infrared small target detection problem has attracted substantial attention. Many works based on local low-rank model have been proven to be very successful for enhancing the discriminability during detection. However, these methods construct patches by traversing local images and ignore the correlations among different patches. Although the calculation is simplified, some texture information of the target is ignored, and targets of arbitrary forms cannot be accurately identified. In this paper, a novel target-aware method based on a non-local low-rank model and saliency filter regularization is proposed, with which the newly proposed detection framework can be tailored as a non-convex optimization problem, therein enabling joint target saliency learning in a lower dimensional discriminative manifold. More specifically, non-local patch construction is applied for the proposed target-aware low-rank model. By combining similar patches, we reconstruct them together to achieve a better generalization of non-local spatial sparsity constraints. Furthermore, to encourage target saliency learning, our proposed saliency filtering regularization term based on entropy is restricted to lie between the background and foreground. The regularization of the saliency filtering locally preserves the contexts from the target and surrounding areas and avoids the deviated approximation of the low-rank matrix. Finally, a unified optimization framework is proposed and solved with the alternative direction multiplier method (ADMM). Experimental evaluations of real infrared images demonstrate that the proposed method is more robust under different complex scenes compared with some state-of-the-art methods.

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Infrared Small Target Detection Based on Patch Image Model with Local and Global Analysis

Cited by 17 publications

References 16 publications

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

TNLRS: Target-Aware Non-Local Low-Rank Modeling With Saliency Filtering Regularization for Infrared Small Target Detection

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