Infrared Small Target Detection Using Local and Nonlocal Spatial Information

Li, Wei; Zhao, Mingjing; Deng, Xiaoya; Li, Lü; Li, Liwei; Zhang, Wenjuan

doi:10.1109/jstars.2019.2931566

Cited by 37 publications

(14 citation statements)

References 28 publications

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“…These methods can locate small targets precisely when the background presents uniformity visually but may fail to deal with abrupt structures in heterogeneous scenes. Local saliency-based methods delineate the difference based on the local region around suspicious targets to enhance the target and suppress background, such as local contrast measure (LCM) [11], novel local contrast method (NLCM) [34], multiscale patch-based measure (MPCM) [12], derivative entropy-based contrast measure (DECM) [35], weighted local difference measure (WLDM) [13], dual-window local contrast method [14]. Such types of methods successfully enhance the dim small target and neglect the smooth areas of background, increasing the detection rate.…”

Section: Related Algorithmsmentioning

confidence: 99%

Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

Zhou

Dai

et al. 2020

Remote Sensing

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Small target detection is a critical step in remotely infrared searching and guiding applications. However, previously proposed algorithms would exhibit performance deterioration in the presence of complex background. It is attributed to two main reasons. First, some common background interferences are difficult to eliminate effectively by using conventional sparse measure. Second, most methods only exploit the spatial information typically, but ignore the structural priors across feature space. To address these issues, this paper gives a novel model combining the spatial-feature graph regularization and l1/2-norm sparse constraint. In this model, the spatial and feature regularizations are imposed on the sparse component in the form of graph Laplacians, where the sparse component is enforced as the eigenvectors of their graph Laplacian matrices. Such an approach is to explore the geometric information in both data and feature space simultaneously. Moreover, l1/2-norm acts as a substitute of the traditional l1-norm to constrain the sparse component, further reducing the fake targets. Finally, an efficient optimization algorithm equipped with linearized alternating direction method with adaptive penalty (LADMAP) is carefully designed for model solution. Comprehensive experiments on different infrared scenes substantiate the superiority of the proposed method beyond 11 competitive algorithms in subjective and objective evaluation.

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Section: Related Algorithmsmentioning

confidence: 99%

Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

Zhou

Dai

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…These methods focus on constructing a local feature descriptor to describe the local discontinuity between small targets and their neighboring pixels. There are some representative local feature descriptors including local contrast measure [4] and its variants [12], [21]- [23], [37], [38], entropy-based contrast measure [24], etc. Notice that some HVS-based methods devise their local descriptors by considering the contrast directional information, which is similar to our contrast consistency assumption.…”

Section: Related Workmentioning

confidence: 99%

Modified Graph Laplacian Model With Local Contrast and Consistency Constraint for Small Target Detection

Xia

Zhao

et al. 2020

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

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The traditional graph Laplacian model has been widely used in many computer vision tasks. The small target detection technique is one of the most challenging computer vision tasks in various practical applications. This paper presents a small target detection method by developing a modified graph Laplacian model with additional constraints. The proposed method is designed based on specific characteristics of small target: global rarity, local contrast, and contrast consistency. First, we analyze the primal graph Laplacian model and exploit its ability to describe global rarity and isolate outliers from nonoutliers. Next, indicators measuring local contrast and contrast consistency are constructed to delineate local characteristics of small targets. Then, we integrate the indicators with the primal graph Laplacian model and propose a modified graph Laplacian model for small target detection. In the confidence maps obtained by the proposed model, small targets are well enhanced, while backgrounds are significantly suppressed. Finally, a small target detection method is proposed based on the graph model. Extensive experiments on various real datasets demonstrate the effectiveness and superiority of the proposed method in detecting small targets.

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“…Existing background estimation algorithms can be divided into nonlocal and local background estimations [11]. The nonlocal background estimation algorithms decompose the entire image into the background and foreground directly.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Han

Liu

et al. 2021

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

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Background estimation is an efficient infrared (IR) small target detection method. However, to deal with unknown targets, the estimation window in existing algorithms should be adjusted to perform multiscale detection, and requires a lot of calculations. Besides, the stages during and after estimation have received wide attention in existing algorithms, but the research on the stages before estimation is insufficient. Moreover, existing algorithms typically regard the maximum value of different orientations as the estimation value. However, when a dim target is adjacent to high brightness background, it is easily submerged. This paper proposes a three-layer estimation window to detect targets of different sizes with only a single-scale calculation. The enhanced closest-mean background estimation method is then proposed and carefully designed before, during and after the estimation. Before estimation, the matched filter is adopted to improve the image signal-to-noise ratio. During estimation, the principle of closest-mean is proposed to suppress high brightness background. After estimation, a ratio-difference operation is performed to enhance the true target and suppress the background simultaneously. A simple checking mechanism is proposed to further improve the detection performance. Experiments on some IR images demonstrate the effectiveness and robustness of the proposed method. Compared with existing algorithms, the proposed method has better target enhancement, background suppression, and computational efficiency. Index Terms-IR small target, background estimation, matched filter, closest-mean, three-layer window.

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Infrared Small Target Detection Using Local and Nonlocal Spatial Information

Cited by 37 publications

References 28 publications

Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

Robust Infrared Small Target Detection via Jointly Sparse Constraint of l1/2-Metric and Dual-Graph Regularization

Modified Graph Laplacian Model With Local Contrast and Consistency Constraint for Small Target Detection

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

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