Infrared Dim Small Target Detection Based on Multi-scale Local Contrast and Multi-scale Gradient Coherence

doi:10.3969/j.issn.1000-1093.2018.08.009

Abstract

Abstract: A novel algorithm based on multi-scale local contrast method and multi-scale gradient coherence method is proposed for the high false alarm of infrared dim small target caused by complex background and heavy clutter. The multi-scale local contrast method is used to enhance the infrared dim small target in infrared image, and the multi-scale gradient coherence method is used to avoid the false alarms caused by complex background and heavy clutter. The proposed algorithm is compared with max-mean, max-median, top-hat, IPI and MGDWIE algorithms in terms of signal-to-noise ratio（SNR）gain, mean absolute value of residual background, detectivity and false alarm rate. The proposed algorithm achieves higher SNR gain, lower mean absolute value of residual background, higher detectivity and lower false alarm rate compared to the baseline algorithms. The experimental results show that the proposed algorithm can effectively reduce the false alarm rate under the disturbance of complex background and heavy clutter.Key

Key words: infrareddimsmalltargetdetection, multi-scalelocalcontrast, multi-scalegradientcoherence, infraredimage

CLC Number:

TP391.413

LIU De-peng， LI Zheng-zhou， ZENG Jing-jie， XIONG Wei-qi， QI Bo. Infrared Dim Small Target Detection Based on Multi-scale Local Contrast and Multi-scale Gradient Coherence[J]. Acta Armamentarii, 2018, 39(8): 1526-1535.

References

［1］ Bi Y, Bai X, Jin T, et al. Multiple feature analysis for infrared small target detection [J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1333-1337.
[2］李云, 宋勇, 赵宇飞, 等. 基于PCNN和改进邻域判决的红外弱小目标检测算法[J]. 兵器装备工程学报, 2018, 39(1): 141-147，182.
LI Yun, SONG Yong,ZHAO Yu-fei, et al. Infrared dim small target detection algorithm based on PCNN and improved neighborhood judgement [J]. Journal of Ordnance Equipment Engineering, 2018, 39(1): 141-147, 182. (in Chinese)
[3］李正周, 侯倩, 戴真,等. 基于空时稀疏表示的红外小目标检测算法[J]. 兵工学报, 2015, 36(7): 1273-1279.
LI Zheng-zhou，HOU Qian，DAI Zhen，et al. Dim moving target detection algorithm based on spatial-temporal sparse representation [J]. Acta Armamentarii, 2015, 36(7): 1273-1279. (in Chinese)
[4］ Li Z Z, Chen J, Hou Q, et al. Sparse representation for infrared dim target detection via a discriminative over-complete dictionary learned online [J]. Sensors, 2014, 14(6): 9451-9470.
[5］ Liu D P, Li Z Z, Liu B, et al. Infrared small target detection in heavy sky scene clutter based on sparse representation [J]. Infrared Physics & Technology, 2017, 85: 13-31.
[6］ Gao C Q, Meng D Y, Yang Y, et al. Infrared patch-image model for small target detection in a single image [J]. IEEE Transactions on Image Processing, 2013, 22(12): 4996-5009.
[7］ Dai Y M, Wu Y Q. Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(8): 3752-3767.
[8］ Dai Y M, Wu Y Q, Song Y, et al. Non-negative infrared patch-image model: robust target-background separation via partial sum minimization of singular values [J]. Infrared Physics & Technology, 2017, 81: 182-194.
[9］ Chen C L P, Li H, Wei Y T, et al. A local contrast method for small infrared target detection [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 574-581.

[10］ Deng H, Sun X P, Liu M L, et al. Infrared small-target detection using multiscale gray difference weighted image entropy [J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(1): 60-72.
[11］ Deng H, Sun X P, Liu M L, et al. Small infrared target detection based on weighted local difference measure [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(7): 4204-4214.
[12］ Deshpande S D, Er M H，Venkateswarlu R, et al. Max-mean and max-median filters for detection of small targets [C]∥Proceedings of SPIE Signal and Data Processing of Small Targets. Bellingham, WA,US: SPIE, 1999:74-83.
[13］ Tom V T，Peli T，Leung M，et al. Morphology-based algorithm for point target detection in infrared backgrounds [C]∥ Proceedings of SPIE Signal and Data Processing of Small Targets. Bellingham, WA,US: SPIE, 1993:2-11.
[14］李越强, 李庶中, 王全喜,等. 一种基于Wiener滤波的海天背景红外目标检测算法 [J]. 兵工学报, 2015, 36(增刊2): 139-143.
LI Yue-qiang，LI Shu-zhong，WANG Quan-xi，et al. An infrared target detection algorithm of sea-sky background based on Wiener filter. [J]. Acta Armamentarii, 2015, 36(S2): 139-143. (in Chinese)
[15］ Bai X Z, Zhou F G, Xie Y C, et al. Enhanced detectability of point target using adaptive morphological clutter elimination by importing the properties of the target region [J]. Signal Processing, 2009, 89(10): 1973-1989.
[16］赵佳佳, 唐峥远, 杨杰,等. 基于图像稀疏表示的红外小目标检测算法[J]. 红外与毫米波学报, 2011, 30(2): 156-161，166.
ZHAO Jia-jia, TANG Zheng-yuan, YANG Jie, et al. Infrared mall target detection based on image sparse representation [J]. Journal of Infrared and Millimeter Waves, 2011, 30(2): 156-161，166. (in Chinese)

第39卷
第8期2018 年8月兵工学报ACTA
ARMAMENTARIIVol.39No.8Aug.2018

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