Infrared Small Target Detection Based on Flux Density and Direction Diversity in Gradient Vector Field

Liu, Depeng; Cao, Lei; Li, Zhengzhou; Liu, Tianmei; Che, Peng

doi:10.1109/jstars.2018.2828317

Cited by 67 publications

(46 citation statements)

References 37 publications

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“…In this section, we firstly select the noise component containing small-faint target. Then, the MFD method [37] is used to extract the target from the noise.…”

Section: Target Extractionmentioning

confidence: 99%

“…It is observed from Figure 2c that the restored slice containing true target is still contaminated by pixel noise. Thus, we use the MFD method [37] to wipe out the noise and enhance the target. The noise component E containing the target is firstly transformed into a gradient vector field by:…”

Section: Selecting Noise Component Containing Targetmentioning

confidence: 99%

“…We propose a small and faint target detection approach based on a non-i.i.d. MoG (NMoG) model [36] and modified flux density (MFD) maple [37]. The noise distributions in different frames (sequences of images) is assumed to follow non-i.i.d.…”

Section: Introductionmentioning

confidence: 99%

“…The target is considered as a kind of noise extracted from the background via an NMoG low-rank matrix factorization (NMoG-LRMF) model, solved by a variational Bayes (VB) algorithm. In a second step, the MFD maple [37] method is used to distinguish the true target from the noise, accounting for the fact that target flux density differs from the noise in infrared gradient vector field. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density

Sun

Yang

et al. 2019

Remote Sensing

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The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is impractical; a state-of-the-art method has been developed to dig out valuable information in the temporal domain and separate small-faint targets from background noise. However, there are still two drawbacks: (1) The mixture of Gaussians (MoG) model assumes that noise of different frames satisfies independent and identical distribution (i.i.d.); (2) the assumption of Markov random field (MRF) would fail in more complex noise scenarios. In real scenarios, the noise is actually more complicated than the MoG model. To address this problem, a method using the non-i.i.d. mixture of Gaussians (NMoG) with modified flux density (MFD) is proposed in this paper. We firstly construct a novel data structure containing spatial and temporal information with an infrared image sequence. Then, we use an NMoG model to describe the noise, which can be separated with the background via the variational Bayes algorithm. Finally, we can select the component containing true targets through the obvious difference of target and noise in an MFD maple. Extensive experiments demonstrate that the proposed method performs better in complicated noisy scenarios than the competitive approaches.

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“…In this section, we firstly select the noise component containing small-faint target. Then, the MFD method [37] is used to extract the target from the noise.…”

Section: Target Extractionmentioning

confidence: 99%

Section: Selecting Noise Component Containing Targetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density

Sun

Yang

et al. 2019

Remote Sensing

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“…Infrared small target detection plays an important role in many applications such as infrared search and tracking system (IRST), automatic target recognition system (ATR) and early warning system [1][2][3]. Due to the long-imaging distance in these applications, targets are usually small and lack of shape and structure information in infrared images, leading to the difficulties in extracting abundant distinctive features of the targets [4][5][6]. Moreover, in practical applications, the small targets are usually drowned in heavy noise and complicated background clutters, which cause more interference to stable detection [7,8].…”

Section: Introductionmentioning

confidence: 99%

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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Infrared small-target detection based on multi-directional multi-scale high-boost response

Peng

Zhang

Huang

et al. 2019

Opt Rev

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Infrared Small Target Detection Based on Flux Density and Direction Diversity in Gradient Vector Field

Cited by 67 publications

References 37 publications

Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density

Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density

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

Infrared small-target detection based on multi-directional multi-scale high-boost response

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