Small target detection based on accumulated center-surround difference measure

Xie, Kai; Fu, Keren; Zhou, Tao; Zhang, Junhao; Yang, Jie; Wu, Qiang

doi:10.1016/j.infrared.2014.07.006

Cited by 47 publications

(8 citation statements)

References 14 publications

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“…more important for practical applications. The single-frame based algorithms can be roughly divided into four categories: background feature-based algorithms [4][5][6][7][8][9], target featurebased algorithms [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], image data structure feature-based algorithms [27][28][29][30][31][32][33], and deep learning-based algorithms [34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…Earlier, Laplace of Gaussian (LoG) filter [10,11] and local contrast measure (LCM) methods [12,13] were used to enhance target saliency. Xie et al [14] proposed an accumulated center-surround difference measure (ACSDM) to separate the target from the background and noise. Take the Gaussian distribution as the property of the IDST, Xu et al [15] developed a facet kernel to detect the small target.…”

Section: Introductionmentioning

confidence: 99%

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Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Liu

Xie

Dong

et al. 2022

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

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Small target detection from infrared remote sensing image is a challenge task. In this paper, a novel local adaptive threshold algorithm combined with heterogeneity and compactness filters is proposed to detect the small target from the infrared remote sensing images. Firstly, the infrared image is filtered by a heterogeneity filter to enhance the target saliency. Then, the enhanced image is filtered by a compactness filter to generate target candidate region map. Finally, for each pixel in the target candidate region, a local adaptive threshold is calculated from the enhanced image to determine whether it is a target pixel or not, and thus the targets are extracted out. The designed heterogeneity filter and compactness filter can effectively suppress the background clutter, enhance the target, and generate target candidate regions. The proposed adaptive thresholding is a local threshold method, which is calculated in a small local window and can effectively reduce the false alarm and missing alarm. Qualitative and quantitative experiments are conducted on synthetic images and real images. The experiment results show that, with good target enhancement and background suppression, and high detection accuracy, the proposed method outperforms other state-of-the-art methods.Index Terms-infrared dim small target (IDST), remote sensing image, adaptive threshold, background suppression, target enhancement.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Small Target Detection From Infrared Remote Sensing Images Using Local Adaptive Thresholding

Liu

Xie

Dong

et al. 2022

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

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show abstract

“…For example, the Laplacian of Gaussian (LoG) filter [16,17] and the difference of Gaussian (DoG) filter [18][19][20][21] use the weighted sum of neighborhood pixels as the background; however, they are sensitive to edges. To suppress the edges better, the improved difference of Gabor (IDoGb) filter [22] and accumulated center-surround difference measure (ACSDM) [23] divide the local area into eight orientations, calculate the weighted sum of neighborhood pixels in different orientations, and then take their maximum value as the background.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation

Han

Liu

et al. 2021

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

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Background estimation is an efficient infrared (IR) small target detection method. However, to deal with unknown targets, the estimation window in existing algorithms should be adjusted to perform multiscale detection, and requires a lot of calculations. Besides, the stages during and after estimation have received wide attention in existing algorithms, but the research on the stages before estimation is insufficient. Moreover, existing algorithms typically regard the maximum value of different orientations as the estimation value. However, when a dim target is adjacent to high brightness background, it is easily submerged. This paper proposes a three-layer estimation window to detect targets of different sizes with only a single-scale calculation. The enhanced closest-mean background estimation method is then proposed and carefully designed before, during and after the estimation. Before estimation, the matched filter is adopted to improve the image signal-to-noise ratio. During estimation, the principle of closest-mean is proposed to suppress high brightness background. After estimation, a ratio-difference operation is performed to enhance the true target and suppress the background simultaneously. A simple checking mechanism is proposed to further improve the detection performance. Experiments on some IR images demonstrate the effectiveness and robustness of the proposed method. Compared with existing algorithms, the proposed method has better target enhancement, background suppression, and computational efficiency. Index Terms-IR small target, background estimation, matched filter, closest-mean, three-layer window.

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“…Moreover, attention-shift-mechanism is applied for searching candidate target regions. An accumulated centersurround difference measure is introduced to detect small targets by Xie et al [12]. They calculate the probability that each pixel belonging to the target by using a measure map.…”

Section: Introductionmentioning

confidence: 99%