Dim small target detection based on high-order cumulant of motion estimation

Fan, Xiangsuo; Xu, Zhihai; Zhang, Jianlin; Huang, Yongmei; Wei, Ziran; Guo, Hongwei

doi:10.1016/j.infrared.2019.04.008

Cited by 21 publications

(10 citation statements)

References 13 publications

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“…James et al [25] designed the small target detection metric using the Markov model theory. Fan et al [26] adopted passion distribution in energy accumulation for small infrared target detection. Kwan et al [27] adopted optical flow techniques to enhance small moving infrared target detection performance, especially for low-quality and long-range infrared videos.…”

Section: ) Single Pixel Association Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

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

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Factors, such as rapid relative motion, clutter background, etc., make robust small aerial target detection for airborne infrared detection systems a challenge. Existing methods are facing difficulties when dealing with such cases. We consider that a continuous and smooth trajectory is critical in boosting small infrared aerial target detection performance. A simple and effective small aerial target detection method for airborne infrared detection system using LightGBM and trajectory constraints is proposed in this paper. First, we simply formulate target candidate detection as a binary classification problem. Target candidates in every individual frame are detected via interesting pixel detection and a trained LightGBM model. Then, the local smoothness and global continuous characteristic of the target trajectory are modeled as short-strict and long-loose constraints. The trajectory constraints are used efficiently for detecting the true small infrared aerial targets from numerous target candidates. Experiments on public datasets demonstrate that the proposed method performs better than other existing methods. Furthermore, a public dataset for small aerial target detection in airborne infrared detection systems is constructed (https://small-infrared-aerial-target-detection.grand-challenge.or g/). To the best of our knowledge, this dataset has the largest data scale and richest scene types within this field.

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Section: ) Single Pixel Association Based Methodsmentioning

confidence: 99%

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Sun

Guo

Zhang

et al. 2021

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

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show abstract

“…Hence, the engineering application of such a method is difficult. Fan [13] uses Poisson distribution to preprocess the images and combines with higher-order accumulation to extract further dim and small targets, thus achieving good detection results. However, the change algorithm requires multiple frames for accumulation, which consumes a longer time.…”

Section: Introductionmentioning

confidence: 99%

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Fan

Chen

et al. 2022

IEEE Access

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In complex non-smooth backgrounds, infrared dim and small target targets generally have lower energy and occupy fewer pixels, and are easily swamped by clutter. To improve the detection capability of dim and small targets in non-smooth scenes, this paper proposes a new dim and small target detection method combining multidirectional gradient difference regularization principal component decomposition model. The method first establishes a new gradient difference regularization to constrain the low-rank subspaces of different image components, then construct a gradient difference regularization-based principal component decomposition model (GDR-PCD), and finally decomposes the model using the overlapping directional multiplier method to obtain the background impedance. The experimental results show that the method performs better in all six sequential image scenes than the traditional algorithm. Furthermore, the detection results verify the algorithm's effectiveness in this paper.INDEX TERMS infrared dim and small target detection, principal component decomposition model, gradient difference regularization principal component decomposition model, overlapping directional multiplier method

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“…Dim and small target signal detection is of great importance for high altitude warning [1][2]. These targets are isolated points in the image, occupy fewer image pixels, lack of relevant texture, and are often accompanied by atmospheric turbulence, clouds and various noise clutter around the target, which makes the target often submerged and brings difficulties to target detection [3][4][5][6][7]. For such detection scenarios, researchers have proposed many feasible detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Dim and Small Target Detection Based on Spiral Gradient Optimization Estimation and High-Order Correlation Enhancement

Fan

Min

et al. 2022

IEEE Access

Self Cite

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In order to reduce the influence of strong halo effect on dim small target detection in daytime, a dim small target detection algorithm based on spiral gradient optimization estimation and high-order correlation enhancement is proposed in this paper. In this paper, we first design a spiral motion model to obtain the local gradient information in the central image point by perturbing the designed motion direction, then estimate the optimal background by establishing a gradient optimization model to achieve background suppression while effectively removing the halo phenomenon. Considering the original high-order correlation model only uses a single pixel for motion correlation, there is insufficient information utilization, an improved high-order correlation energy enhancement model is proposed to enhance the target signal, the algorithm first constructs an attention discrimination model based on inner and outer windows to obtain the salient region of the image, and then carries out multi frame high-order motion correlation of neighborhood blocks to enhance the target energy. After experiments, it is shown that compared with the traditional algorithm, the algorithm proposed in this paper can effectively weaken the halo effect while suppressing most of the background and can effectively enhance the local signal-to-noise ratio of the target.INDEX TERMS Dim and small target detection; Background suppression; Spiral gradient; High order correlation; Energy enhancement.

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Dim small target detection based on high-order cumulant of motion estimation

Cited by 21 publications

References 13 publications

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Small Aerial Target Detection for Airborne Infrared Detection Systems Using LightGBM and Trajectory Constraints

Infrared Dim and Small Target Detection Algorithm Combining Multiway Gradient Regularized Principal Component Decomposition Model

Dim and Small Target Detection Based on Spiral Gradient Optimization Estimation and High-Order Correlation Enhancement

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