Infrared small target detection based on directional zero-crossing measure

Zhang, Xiangyue; Ding, Qinghai; Luo, Haibo; Bu, Hui; Chang, Zheng; Zhang, Junchao

doi:10.1016/j.infrared.2017.09.016

Cited by 23 publications

(11 citation statements)

References 24 publications

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“…Li and Zhang [34] introduce a local steeling kernel representation to distinguish small targets from cloud edges. Besides, Zhang et al [13], Bai and Bi [14], and Qi et al [15] employ the derivative information calculated on the facet model to suppress cloud edges.…”

Section: A Single-frame Detection Methodsmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Jiachen Yang. in infrared small target detection, several local contrast methods have been proposed weighted by cloud edge suppression factor in recent years, such as the entropy weighted methods [5], [6], Total Variation (TV) weighted method [7], structure tensor weighted methods [8]- [10], gradient information [11], [12] and derivative information calculated based on facet model [13]- [17] for cloud edge removal. It is worth mention that the MPCM method [4] considers the distribution difference between a target and cloud edges, and utilizes the minimized product in diagonal directions as the final enhancement results.…”

Section: Introductionmentioning

confidence: 99%

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Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Gao

Lin

2019

IEEE Access

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Infrared small target detection is challenging due to the various background and low signalto-clutter ratios. Considering the information deficiency faced by single spatial or temporal information, we construct a low false alarm spatial and temporal filter for infrared small target detection. A multiscale patch-based contrast measure is first used to suppress background and remove cloud edges at a coarse level. Then, a temporal variance filter is used to remove small broken cloud regions and suppress noise at a fine level. By integrating these two methods, infrared small targets can be extracted accurately and robustly using an adaptive threshold segmentation. The experimental results indicate that our proposed method can robustly detect small infrared targets with a low false alarm rate. INDEX TERMS Infrared image sequence, small target detection, local contrast, temporal profile.

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Section: A Single-frame Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Using a Temporal Variance and Spatial Patch Contrast Filter

Gao

Lin

2019

IEEE Access

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“…We first set up a threshold 0 Th that is greater than zero, and then find all the candidate points satisfying the threshold condition along the filtering direction in each FODD map. After that, the zero-crossing points are searched in 0 N points within this candidate point as the starting point by using (7).…”

Section: B the Dps Methodsmentioning

confidence: 99%

“…Therefore, based on a phenomenon that a small target is an isotropic Gaussian distribution at a long distance, while clutters show different characteristics in different directions, we propose a new infrared small target detection method which called a directional-progressive search (DPS) method. Our method is based on the FODD filter which proposed by Zhang et al [7]. By using this FODD filter, we search zero-crossing points step by step on different directional sub-images to achieve the goal of extracting the target gradually.…”

Section: Introductionmentioning

confidence: 99%

A Directional-Progressive Search Method for Infrared Small Target Detection

Zhang

Ding²,

Luo

et al. 2018

2018 IEEE 3rd Optoelectronics Global Conference (OGC)

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Infrared small target detection plays a key role in infrared precision guidance and infrared early-warning system. It has been a difficult problem for researchers to study on how to detect targets accurately at a long distance as early as possible. Most of the existing algorithms can detect small targets in simple backgrounds, but they would fail on the detection when the background clutters are chaotic and the signal to clutter ratio (SCR) is low. Therefore, we propose a new infrared small target detection method which called a directional-progressive search (DPS) method. Our method derives from a fact that a small target is an isotropic Gaussian distribution at a long distance, while clutters show different characteristics in different directions. Based on this difference, we decompose the original image into first-order sub-images with different directions by using a first-order directional derivative (FODD) filter. Then zero-crossing points are detected in each direction step by step to distinguish small targets and background clutters. After screening progressively, the positions where existing zero-crossing points in every subimage can be confirmed as targets. Experimental results show that our method acquires higher detection rates and lower false alarm rates compared with other methods. At the same time, our method can still keep better performance under various complex backgrounds. The robustness of our method is strong.

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“…By representing the original image in multiple scales, the SCR gain can be enhanced obviously. Zhang et al [17] proposed a filter named first-order directional derivative filter (FODD). Different from the above methods, instead of a second-order operation, a first-order operation is carried out on the image.…”

Section: Introductionmentioning

confidence: 99%