A Composite Kernel Regression Method Integrating Spatial and Gray Information for Infrared Small Target Detection

Gu, Yanfeng; Wang, Chen; Liu, Baoxue; Liu, Zhenlin; Zhang, Ye

doi:10.1109/pcspa.2010.269

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Small targets are often submerged in nonconstant complex backgrounds with low signal-noise ratios and low contrast. Moreover, infrared small targets always have unremarkable features, uncertain brightness, and weak intensity because of the long imaging distance in the atmosphere [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Infrared Small Target Detection Method via Precise Feature Matching and Scale Selection Strategy

Yan

Xin

et al. 2020

IEEE Access

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Infrared small target detection is a crucial and challenging topic for various applications. In recent years, the spectrum scale space (SSS) algorithm has shown considerable potential in the field of target detection. However, the SSS algorithm is prone to high false alarm rates in infrared small target detection scenarios with complex background. This paper proposes an improved SSS (ISSS) algorithm via precise feature matching and scale selection strategy for efficient infrared small target detection, which includes background suppression, feature matching and optimal scale selection three stages. In the background suppression stage, a matrix decomposition method named inexact augmented Lagrange multiplier (IALM) algorithm is used to extract the sparse image matrix from the original image as the target foreground image. In the feature matching stage, the 16 elaborate Gaussian kernel functions convolve with the the amplitude spectrum of target foreground image to generate 16 scale saliency maps that precisely match the feature of small targets. In the optimal scale selection stage, a few proper candidate scale maps are screened out according to the difference between the pixel values of the target area and the background clutters, in which the target area was more highlighted, and the scale map corresponding to the maximum value of local information entropy of the candidate saliency map is the final detection result map. We mainly made three contributions: First, IALM algorithm is utilized as a preprocessing step, and we have verified it is indispensable in eliminating most backgrounds with self-correlation property. Second, an elaborate scale division strategy is proposed to obtain multi-scale saliency maps that match the feature of infrared small targets precisely. Third, the gray value difference and the maximum value of local information entropy are defined and used as the judgment criteria for optimal scale selection. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art techniques, especially on infrared images with thick clouds and high-brightness buildings. INDEX TERMS Infrared small target detection, improved spectrum scale space, matrix decomposition.

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Section: Introductionmentioning

confidence: 99%

Multi-Scale Infrared Small Target Detection Method via Precise Feature Matching and Scale Selection Strategy

Yan

Xin

et al. 2020

IEEE Access

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show abstract

“…[3][4][5][6] Thus, a class of background estimation methods that utilize gray value differences between the small target and its surroundings is proposed. For example, Chen et al proposed a local contrast method (LCM) 3 and kernel regression model, [7][8][9] which use local gray value contrast of small target and its surroundings to distinguish the target from background. Since the edges also have a high level contrast with their surroundings, the method could only segregate target from the image only containing small target and homogeneous background.…”

Section: Introductionmentioning

confidence: 99%

Small target detection using edge-preserving background estimation based on maximum patch similarity

Wang

Yue

et al. 2017

International Journal of Advanced Robotic Systems

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Infrared small target detection is widely applied in lots of practical applications, but due to the complicated edges in practical scenarios, most existing detection algorithms usually lead to many false alarms and cannot detect the target accurately. Addressing this problem, a novel edge-preserving background estimation method based on maximum patch similarity was proposed in this article. At first, we will propose an improved local adaptive contrast measure to suppress the pixel-size electronic noises. Then, maximum patch similarity with minimum improved local adaptive contrast measure can be utilized to preserve the edge in the estimated background. Finally, we can obtain target image by filtering the background image from original image and use adaptive threshold segmentation to detect the small target in our target image. It is shown from experiments that our proposed method has better detection results in diverse infrared images, improving signal-to-clutter ratio gain and background suppression factor of the images significantly and efficiently.

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“…Lots of background estimation methods for small target detection are proposed to segregate background and foreground for small target detection in last decade [1][2][3][4][5][6][7][8][9][10][11][12]. In these background estimation methods, local gray level distribution are used to predict background [1].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised learning based edge-preserving background estimation for small target detection

Bai

Wang

Song

et al. 2015

Infrared Physics & Technology

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A Composite Kernel Regression Method Integrating Spatial and Gray Information for Infrared Small Target Detection

Cited by 3 publications

References 11 publications

Multi-Scale Infrared Small Target Detection Method via Precise Feature Matching and Scale Selection Strategy

Multi-Scale Infrared Small Target Detection Method via Precise Feature Matching and Scale Selection Strategy

Small target detection using edge-preserving background estimation based on maximum patch similarity

Semi-supervised learning based edge-preserving background estimation for small target detection

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