Infrared small target and background separation via column-wise weighted robust principal component analysis

Dai, Yawen; Wu, Yiquan; Shen, Yu

doi:10.1016/j.infrared.2016.06.021

Cited by 119 publications

(64 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We call this phenomenon the rare structure effect. In contrast, the nonlocal prior methods [26], [29], [30] suffer from the salient edge residuals. Its intrinsic reason is because the strong edge is also a sparse component as the same as the target due to lack of sufficient similar samples.…”

Section: B Motivationmentioning

confidence: 99%

“…[26] and Ref. [29] neglect is the fact that the small target is always brighter than its neighborhood environment in infrared images due to the physical imaging mechanism [43]. Therefore, besides the sparsity constraint [44], [45] of the target patch-tensor, it is reasonable to assume that all the entries in T are non-negative.…”

Section: A Reweighted Infrared Patch-tensor Modelmentioning

confidence: 99%

“…Baselines and Parameter settings. The proposed algorithm is compared with ten state-of-the-art solutions, including three filtering based methods (Max-Median [10], Top-Hat [48], TDLMS [9]), three HVS based methods (PFT [49], MPCM [19], WLDM [23]), and four recently developed lowrank methods (IPI [26], PRPCA [50], WIPI [29], NIPPS [30]). Tab.…”

Section: A Experimental Setupmentioning

confidence: 99%

See 2 more Smart Citations

Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Dai

2017

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

Self Cite

296

173

View full text Add to dashboard Cite

Many state-of-the-art methods have been proposed for infrared small target detection. They work well on the images with homogeneous backgrounds and high-contrast targets. However, when facing highly heterogeneous backgrounds, they would not perform very well, mainly due to: 1) the existence of strong edges and other interfering components, 2) not utilizing the priors fully. Inspired by this, we propose a novel method to exploit both local and non-local priors simultaneously. Firstly, we employ a new infrared patch-tensor (IPT) model to represent the image and preserve its spatial correlations. Exploiting the target sparse prior and background non-local self-correlation prior, the target-background separation is modeled as a robust low-rank tensor recovery problem. Moreover, with the help of the structure tensor and reweighted idea, we design an entry-wise localstructure-adaptive and sparsity enhancing weight to replace the globally constant weighting parameter. The decomposition could be achieved via the element-wise reweighted higher-order robust principal component analysis with an additional convergence condition according to the practical situation of target detection. Extensive experiments demonstrate that our model outperforms the other state-of-the-arts, in particular for the images with very dim targets and heavy clutters.Index Terms-infrared small target detection, infrared patchtensor model, reweighted higher-order robust principal component analysis, non-local self-correlation prior, local structure prior. Y. Dai is with the College

show abstract

Section: B Motivationmentioning

confidence: 99%

Section: A Reweighted Infrared Patch-tensor Modelmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

See 1 more Smart Citation

Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Dai

2017

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

Self Cite

296

173

View full text Add to dashboard Cite

show abstract

“…Therefore, it is a challenging problem to separate small targets from complicated backgrounds without any false alarms in infrared noisy images [9,10]. To solve this problem, many methods were proposed in recent processing a scene with strong edges [38]. Although the following methods [39][40][41][42][43] obtain a remarkable progress to remove the edge residuals, they can hardly eliminate the strong local clutters of various shapes completely by employing a specific sophisticated norm to replace the nuclear norm.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Liu

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

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...

show abstract

“…Therefore, the target detection problem can be converted into estimating the sparse matrix and the low rank matrix. Motivated by the IPI model, Dai et al [7] proposed a weighted IPI (WIPI) model. In this method, more structural information is added to the WIPI model and different weights are adaptively assigned to different patches.…”

Section: Introductionmentioning

confidence: 99%

Infrared small target detection based on directional zero-crossing measure

Zhang

Ding

Luo

et al. 2017

Infrared Physics & Technology

View full text Add to dashboard Cite

Infrared small target and background separation via column-wise weighted robust principal component analysis

Cited by 119 publications

References 33 publications

Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection

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

Infrared small target detection based on directional zero-crossing measure

Contact Info

Product

Resources

About