Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

Fan, Xiangsuo; Xu, Zhihai; Zhang, Jianlin; Huang, Yongmei; Peng, Zhenming

doi:10.1155/2017/4572147

Cited by 10 publications

(8 citation statements)

References 24 publications

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“…Cirrus has similar visual characteristics with these small targets, so the infrared small targets detection method can be introduced into cirrus detection. The detection methods of infrared small targets mainly include methods based on filtering and visual features [21]- [27], methods based on background continuity [28], [29] and methods based on optimization.…”

Section: A Related Workmentioning

confidence: 99%

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

et al. 2020

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Infrared small target detection systems are an important part of space infrared imaging satellites. However, small infrared target detection is often affected by cirrus false alarm sources with similar grayscales. In this paper, an infrared cirrus detection method based on the tensor robust principal component analysis model (TRPCA) is proposed. The method treats multiple bands of remote sensing data as tensors, but classical tensor nuclear norms cannot represent the tensor rank well; therefore, we use tensor multi-mode expansion sum nuclear norm (TMESNN) to approximate the tensor rank better. First, a set of Landsat-8 data is transformed into a tensor model, and a TRPCA model is constructed by TMESNN and the 1 L norm. Then, this model is solved by Ket augments and the alternating direction method of multipliers (ADMM). Finally, Mallat wavelet transform is used to supplement information and remove clutter, and the final detection result is obtained by adaptive threshold segmentation. Compared with other optimizationbased methods, this method has better detection performance and accuracy.

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Section: A Related Workmentioning

confidence: 99%

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

et al. 2020

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“…A range of machine learning-based methodologies can be used for small target detection [22][23][24][25][26][27][28][29][30][31][32]. Gu et al [23] proposed a method to apply a constant false alarm rate (CFAR) detector to the target region after suppressing the clutter by predicting the background through a kernel-based nonparametric regression method.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Gray-Temperature Fusion-Based Deep Learning Architecture for Far Infrared Small Target Detection

Ryu

Kim

2019

Journal of Sensors

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This paper proposes the end-to-end detection of a deep network for far infrared small target detection. The problem of detecting small targets has been a subject of research for decades and has been applied mainly in the field of surveillance. Traditional methods focus on filter design for each environment, and several steps are needed to obtain the final detection result. Most of them work well in a given environment but are vulnerable to severe clutter or environmental changes. This paper proposes a novel deep learning-based far infrared small target detection method and a heterogeneous data fusion method to solve the lack of semantic information due to the small target size. Heterogeneous data consists of radiometric temperature data (14-bit) and gray scale data (8-bit), which includes the physical meaning of the target, and compares the effects of the normalization method to fuse heterogeneous data. Experiments were conducted using an infrared small target dataset built directly on the cloud backgrounds. The experimental results showed that there is a significant difference in performance according to the various fusion methods and normalization methods, and the proposed detector showed approximately 20% improvement in average precision (AP) compared to the baseline constant false alarm rate (CFAR) detector.

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“…A large number of methods have been developed to address the issues of small target detection. ese methods can be roughly classified into two categories: single-frame detection [5][6][7][8][9][10][11][12][13][14][15][16] and sequential multiframe detection [17][18][19]. Recently, Gao et al [17] employed the mixture of the Gaussians model [20] with the Markov random field to model the complex noise of which the target is assumed as a component.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Fang

Chen

Liu

et al. 2020

Mathematical Problems in Engineering

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Infrared small target detection plays an important role in infrared search and tracking systems applications. It is difficult to perform target detection when only a single image with complex background clutters and noise is available, where the key is to suppress the complex background clutters and noise while enhancing the small target. In this paper, we propose a novel model for separating the background from the small target based on nonlocal self-similarity for infrared patch-image. A total variation-based regularization term for the small target image is incorporated into the model to suppress the residual background clutters and noise while enhancing the smoothness of the solution. Furthermore, a reweighted sparse constraint is imposed for the small target image to remove the nontarget points while better highlighting the small target. For higher computational efficiency, an adapted version of the alternating direction method of multipliers is employed to solve the resulting minimization problem. Comparative experiments with synthetic and real data demonstrate that the proposed method is superior in detection performance to the state-of-the-art methods in terms of both objective measure and visual quality.

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Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

Cited by 10 publications

References 24 publications

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

Cirrus Detection Based on Tensor Multi-Mode Expansion Sum Nuclear Norm in Infrared Imagery

Heterogeneous Gray-Temperature Fusion-Based Deep Learning Architecture for Far Infrared Small Target Detection

Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

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