Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis

Wang, Chuanyun; Qin, Shiyin

doi:10.1016/j.infrared.2015.01.017

Cited by 90 publications

(41 citation statements)

References 36 publications

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“…In reference [10], when the noise can be approximated as additive, and the infrared small target image can be seen as a linear combination of target image, background image, and noise image. This assumption is also widely used in future models [30][31][32][33][34]. This model can be represented by the following equation.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

et al. 2019

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The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…He et al [30] proposed the low-rank representation (LRR) method. Wang C [31] proposed an adaptive target-background separation (T-BS) model. Dai Y [32] applied local steering kernel [33] to the penalty factor and proposed the weighted infrared patch image (WIPI) model.…”

Section: Detection Before Trackmentioning

confidence: 99%

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

et al. 2019

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“…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%

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

333

169

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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

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“…In addition, TIC can provide proper information under local or global darkness, for example, shadows or darkness caused by damaged lighting. Recently, thermal images from TIC are studied to recognize pattern and motion remotely [24]. The cameras will detect hot objects as well as thermal reflections off of surfaces.…”

Section: Model-based Featuresmentioning

confidence: 99%

Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images

Kim

Lattimer

2016

Journal of Sensors

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Locating a fire inside of a structure that is not in the direct field of view of the robot has been researched for intelligent firefighting robots. By classifying fire, smoke, and their thermal reflections, firefighting robots can assess local conditions, decide a proper heading, and autonomously navigate toward a fire. Long-wavelength infrared camera images were used to capture the scene due to the camera’s ability to image through zero visibility smoke. This paper analyzes motion and statistical texture features acquired from thermal images to discover the suitable features for accurate classification. Bayesian classifier is implemented to probabilistically classify multiple classes, and a multiobjective genetic algorithm optimization is performed to investigate the appropriate combination of the features that have the lowest errors and the highest performance. The distributions of multiple feature combinations that have 6.70% or less error were analyzed and the best solution for the classification of fire and smoke was identified.

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Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis

Cited by 90 publications

References 36 publications

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

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

Feature Selection for Intelligent Firefighting Robot Classification of Fire, Smoke, and Thermal Reflections Using Thermal Infrared Images

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