Infrared Small Target Detection Based on the Weighted Strengthened Local Contrast Measure

Han, Jinhui; Moradi, Saed; Faramarzi, Iman; Zhang, Honghui; Zhao, Qian; Zhang, Xiaojian; Li, Nan

doi:10.1109/lgrs.2020.3004978

Cited by 202 publications

(73 citation statements)

References 29 publications

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“…Based on the above ideas, Chen et al [15] proposed a local contrast method (LCM) for infrared small target detection. Further, many improved LCM (ILCM) methods are proposed [16], such as multiscale relative LCM [17], weighted strengthened local contrast measure (WSLCM) [18], multiscale tri-layer local contrast measure (TLLCM) [19], and Gaussian scalespace enhanced LCM [20]. However, the performance of these methods would degrade for complex background cases that the clutters are similar to the target in saliency maps.…”

Section: B Hvs-based Infrared Small Target Detection Methodsmentioning

confidence: 99%

“…To further evaluate the effectiveness of the proposed method, we compare its performance with ten state-of-theart methods, which are BS-based methods (Top-Hat [12]), HVS-based methods (WSLCM [18], TLLCM [19]), and recently developed LRSD-based methods (IPI [22], NRAM [26], SMSL [29], TV-PCP [31], RIPT [4], PSTNN [5], STTV-WNIPT [8]). Table I summarizes all the methods involved in the experiments and their detailed parameter settings.…”

Section: A Evaluation Metrics and Baseline Methodsmentioning

confidence: 99%

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Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Liu,

Yang,

et al. 2021

Preprint

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Infrared small target detection plays an important role in many infrared systems. Recently, many infrared small target detection methods have been proposed, in which the lowrank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection. In our method, NTLA adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we use the asymmetric spatial-temporal total variation (ASTTV) to thoroughly describe background feature, which can achieve good background estimation and detection in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness strength for spatial and temporal regularization. We develop an efficient algorithm to find the optimal solution of the proposed model. Compared with some state-of-the-art methods, the proposed method achieve an improvement in different evaluation metrics. Extensive experiments on both synthetic and real data demonstrate the proposed method provide a more robust detection in complex situations with low false rates.

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Section: B Hvs-based Infrared Small Target Detection Methodsmentioning

confidence: 99%

Section: A Evaluation Metrics and Baseline Methodsmentioning

confidence: 99%

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Liu,

Yang,

et al. 2021

Preprint

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show abstract

“…The entire procedure of LSM is given in Algorithm 1. (2) for x = 1 : row do (3) for y = 1 : col do (4) Obtain the local slices R 11 and R 2m s by Equations ( 1) and ( 2); (5) Obtain the normalized slices R nor1 and R m nor s by Equations ( 3) and ( 4); (6) Calculate the matching coefficient r 1 (x,y) and determine the R 2m max by Equations ( 5)-( 7); (7) Construct the spatial-temporal joint model between I b and I b+l and calculate I v1 (x, y) by Equations ( 8)-( 14); (8) Conduct reverse matching and obtain R 31 by Equations ( 15)-( 17); (9) Calculate the normalized slice of R 31 by Equation ( 3); (10) Calculate the matching coefficient r 2 (x, y) by Equation ( 5); (11) Construct the spatial-temporal joint model between I b−l and I b and calculate I v2 (x, y) by Equations ( 8)-( 14); (12) Calculate the saliency map value I map (x, y) by Equations ( 18) and ( 19); (13) end for (14) end for (15) Obtain the saliency map I map ; (16) Calculate the adaptive threshold T by formula Equation ( 20); (17) Output the position of the aerial target.…”

Section: Adaptive Threshold Segmentationmentioning

confidence: 99%

“…The local contrast method (LCM) proposed by Chen et al [9] attracts much attention for the concise structure. Additionally, a great number of LCM-based methods [10][11][12] working on the ground-based platform have subsequently been proposed and detect small targets under complex backgrounds. Moreover, most space-based detection methods, such as local blob-like contrast map and local gradient map (LBCM-LGM) [13], neighborhood saliency map (NSM) [14], spatial-temporal local contrast method (STLCM) [2], and spatialtemporal local contrast filter (STLCF) [15], are LCM based.…”

Section: Introductionmentioning

confidence: 99%

Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

Chen

Rao

Chen

et al. 2022

Sensors

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The feature of a space-based infrared signal is that the intensity of clutter is much stronger than that of an aerial target. Such a feature poses a great challenge to aerial target detection since the existing infrared target detection methods are prone to enhance clutter but ignore the real target, which results in missed detection and false alarms. To tackle the challenge, we propose a concise method based on local spatial–temporal matching (LSM). Specifically, LSM mainly consists of local normalization, local direction matching, spatial–temporal joint model, and inverse matching. Local normalization aims to enhance the target to the same strength as the clutter, so that the weak target will not be ignored. After normalization, a direction-matching step is applied to estimate the moving direction of the background between the basic frame and referenced frame. Then the spatial–temporal joint model is constructed to enhance the target and suppress strong clutter. Similarly, inverse matching is conducted to further enhance the target. Finally, a salience map is obtained, on which the aerial target is extracted by the adaptive threshold segmentation. Experiments conducted on four space-based infrared datasets indicate that LSM handles the above challenge and outperforms seven state-of-the-art methods in space-based infrared aerial target detection.

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“…The latter tends to be inefficient and is often unable to perform real-time end-to-end detection [3]. Traditional methods for detecting infrared small targets based on single-frame detection include filter-based methods [4,5], local contrast-based methods [6][7][8][9], and low-rankbased methods [10][11][12][13]. However, these traditional methods based on handcraft fixed sliding windows, step sizes, and fixed hyperparameters are incapable of detecting targets accurately when the characteristics of the real scene (e.g., target size, shape, and background clutter) change significantly from those expected.…”

Section: Introductionmentioning

confidence: 99%

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

et al. 2021

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Detecting infrared small targets lacking texture and shape information in cluttered environments is extremely challenging. With the development of deep learning, convolutional neural network (CNN)-based methods have achieved promising results in generic object detection. However, existing CNN-based methods with pooling layers may lose the targets in the deep layers and, thus, cannot be directly applied for infrared small target detection. To overcome this problem, we propose an enhanced asymmetric attention (EAA) U-Net. Specifically, we present an efficient and powerful EAA module that uses both same-layer feature information exchange and cross-layer feature fusion to improve feature representation. In the proposed approach, spatial and channel information exchanges occur between the same layers to reinforce the primitive features of small targets, and a bottom-up global attention module focuses on cross-layer feature fusion to enable the dynamic weighted modulation of high-level features under the guidance of low-level features. The results of detailed ablation studies empirically validate the effectiveness of each component in the network architecture. Compared to state-of-the-art methods, the proposed method achieved superior performance, with an intersection-over-union (IoU) of 0.771, normalised IoU (nIoU) of 0.746, and F-area of 0.681 on the publicly available SIRST dataset.

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Infrared Small Target Detection Based on the Weighted Strengthened Local Contrast Measure

Cited by 202 publications

References 29 publications

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Local Spatial–Temporal Matching Method for Space-Based Infrared Aerial Target Detection

EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection

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