Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure

Han, Jinhui; Liang, Kun; Zhu, Xindong; Zhao, Jie; Ling, Zhao

doi:10.1109/lgrs.2018.2790909

Cited by 351 publications

(150 citation statements)

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“…Please note that most of these baseline methods are proposed in the last two years, and they can represent the highest level of infrared small target detection in the current period. The employed baseline methods are Min-Local-LoG method [60], the LS-SVM-based method [25], the multiscale patch based contrast measure (MPCM) [46], the high-boost-based multiscale local contrast measure (HB-MLCM) [47], the multiscale weighted local contrast measure (MWLCM) [48], the derivative entropy based contrast measure (DECM) [49], and the multiscale relative local contrast measure (RLCM) [50].…”

Section: Methodsmentioning

confidence: 99%

“…Obviously N g determines the length of the chain, which is closely related to the size of small targets. According to Society of Photo-Optical Instrumentation Engineers (SPIE), a small target is defined to have a total spatial extent of less than 80 pixels [44,50], which means the target size is less than 9 × 9 (pixels) and the radius of the target is less than 5 pixels. To guarantee the chain can exceed the scope of target region when it grows from the target center, in this paper, we set N g = 5 so that the length of the chain is larger than the radius of small targets.…”

Section: Stop Criterionmentioning

confidence: 99%

“…TP (true positive) represents the number of detected true targets, and AP (actual positive) represents the total number of targets. FP (false positive) represents the number of detected false targets, and AN (actual negative) is commonly defined as the total number of pixels in one frame in this research field [49,50]. Through choosing different segmentation thresholds, we can get different points in the TPR-FPR space (also called ROC space).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…In 2013, Chen et al created a feature called the local contrast measure (LCM) to define the local contrast between the targets and background in infrared images [44]. Since then, plenty of other definitions of the local contrast, such as the improved difference of Gabor filter [45], the multiscale patch-based contrast measure (MPCM) [46], the high-boost-based multiscale local contrast measure (HBMLCM) [47], the multiscale weighted local contrast measure (MWLCM) [48], the derivative entropy-based contrast measure (DECM) [49], the relative local contrast measure (RLCM) [50], the Gaussian scale-space enhanced local contrast measure (GSS-ELCM) [51], the homogeneity-weighted local contrast measure (HWLCM) [52], and so on, were proposed. The above models compute the local feature value at each position by sliding a rectangle window, and they are usually concise and thus have a fast running speed.…”

Section: Introductionmentioning

confidence: 99%

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Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Huang

Liu

et al. 2019

Remote Sensing

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Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes.Remote Sens. 2020, 12, 47 2 of 22 decades, and they can be generally grouped into two categories: track-before-detect (TBD)-based methods and detect-before-track (DBT)-based methods.The TBD-based methods, such as pipeline filtering [11], hypothesis testing [12], 3-D matched filtering [13], temporal profile filtering [14], dynamic programming [15] and so on, try to use the grayscale consistency and the trajectory continuity of the targets in consecutive frames so as to discriminate the small targets from noise [16][17][18][19]. These methods are based on two assumptions; one is that the motion model of the target is known, the other is that the background motion is slow. In ideal conditions, both assumptions are satisfied; the energy of the targets is accumulated in adjacent frames, and the difference between targets and noise is increased. As a result, the TBD-based methods perform well in low signal-to-clutter conditions. However, in practical applications, we can hardly acquire the precise motion model of the targets, and the backgrounds can move fast when the infrared detector is in a moving platform. Therefore, both of the two assumptions could fail in real situations, and the performance of the TBD-based methods could degrade significantly [20]. Meanwhile, high time and storage requirements also make these TBD approaches unsuited to large-scale engineering projects [21].Compared with TBD-based methods, DBT-bas...

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Section: Methodsmentioning

confidence: 99%

Section: Stop Criterionmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Huang

Liu

et al. 2019

Remote Sensing

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“…Chen et al [16] provided a clue to simply use local contrast measurement as enhancement factor to pop out small targets and suppress background. After that, there are a series of improved schemes proposed one after another, such as improved/novel local contrast method (ILCM/NLCM) [17,18], relative local contrast measure (RLCM) [19], local saliency map (LSM) [20], weighted local difference measure (WLDM) [21], multiscale patch-based contrast measure (MPCM) [22] and its improved versions [23,24]. Furthermore, the local entropy quantifying the complex degree of local gray distribution has been absorbed into the local contrast method to highlight small targets [25,26].…”

Section: Introductionmentioning

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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Infrared Small Target Detection Based on Weighted Variation Coefficient Local Contrast Measure

Wei

et al. 2021

Lecture Notes in Computer Science

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Infrared Small Target Detection Utilizing the Multiscale Relative Local Contrast Measure

Cited by 351 publications

References 14 publications

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

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

Infrared Small Target Detection Based on Weighted Variation Coefficient Local Contrast Measure

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