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收稿：2019-03-14，
修回：2019-6-18，
录用：2019-6-25，
纸质出版：2019-12-16
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目的
2
复杂背景中的红外小目标检测易受背景杂波与噪声的干扰，直接利用现有的低秩约束与稀疏表示联合模型存在准确率低、虚警率高及检测速度慢等不足。为了解决这些问题，提出一种基于多尺度红外超像素图像模型的小目标检测方法。
方法
2
首先，采用超像素方法分割原始红外图像，得到无重叠区域的超像素图像，充分利用红外图像的局部空间相关性；然后，引入多尺度理论，融合多个不同尺度下检测的目标图像，增强该方法检测不同尺寸目标的稳健性。
结果
2
针对多幅不同场景下的红外小目标图像进行了实验验证，并选取信杂比增益、背景抑制因子及检测时间作为定量评价指标，以此衡量背景抑制效果及算法运行速度。大量实验结果表明，与Top-Hat、Max-Median、二维最小均方、局部显著性图、红外块图像、加权红外块图像等方法相比，本文方法能有效地去除各种干扰，在背景抑制方面具有更好的效果，且所得背景抑制因子为其他方法的数十倍；与同类方法相比，红外超像素图像模型减少了至少78.2%的检测时间。
结论
2
本文将超像素图像分割与多尺度理论引入低秩约束与稀疏表示联合模型，能够取得更好的背景抑制效果，并且可以适应不同大小目标的检测，实现复杂背景中红外小目标的准确检测。
Objective
2
Infrared small-target detection is a key technology in precision guidance. It is crucial in aircraft infrared search and tracking systems
infrared imaging and guidance systems
and early warning systems for military installations. However
infrared small-target detection in complex backgrounds still encounters challenges. First
due to the long imaging distance
the target is usually very dim and small and lacks a concrete structure and texture information. Second
when strong background clutter and noise exist
such targets are often buried in the background with a low signal-to-clutter ratio. Hence
the issue remains difficult and challenging. Meanwhile
utilizing the existing low rank constraint and sparse representation joint model directly has disadvantages of low accuracy
high false alarm rate
and slow detection. To solve these problems
a small-target detection method based on the multiscale infrared superpixel-image model is proposed.
Method
2
The method of constructing an infrared-patch image in prior literature involves setting the sliding window to slide from up to down and left to right of the image at a certain step size. The gray value of each pixel in the sliding window is rearranged into a column vector when it slides to each position. The matrix composed of these column vectors is called the patch image. However
in the process of constructing such a patch image in this manner
the proportion of overlapping area between sliding windows is large
resulting in a high degree of information redundancy. In addition
the constructed patch image has high dimensionality
which leads to a large amount of calculation. To overcome these deficiencies
the superpixel method is adopted to segment the original infrared image and obtain superpixel images with no overlapping area. The method makes full use of the local spatial correlation of the infrared image and avoids the computational burden caused by redundant information. Moreover
introducing multiscale theory then merging the target images detected at different scales can further improve the robustness of the algorithm for detecting targets of different sizes.
Result
2
First
experiments are conducted on many infrared small target images with varying situations and levels of noise. Experimental results demonstrate that from the perspective of subjective visual evaluation
the proposed method is robust to different scenes and noise. Experiments are also conducted to verify two aspects
namely
background suppression effect and detection speed. The signal-to-clutter ratio gain and background suppression factor are selected as quantitative evaluation indicators for the background suppression effect. Experimental results reveal that compared with the Top-Hat
Max-Median
two-dimensional least mean square
local saliency map
infrared patch-image
and weighted infrared patch-image methods
the proposed method can effectively eliminate various interferences
exerts a superior effect on background suppression
and can accurately detect infrared small targets in complex backgrounds simultaneously. The background suppression factor of the proposed method is several tens of times that of other methods
and the infrared superpixel-image model reduces the detection time by at least 78.2% compared with similar methods.
Conclusion
2
In this study
superpixel image segmentation and multiscale theory are introduced into the low rank constraint and sparse representation joint model. The model can achieve an advantageous background suppression effect and good adaptability to the target size when applied to infrared small-target detection in complex backgrounds. In addition
the proposed method of infrared small-target detection at different scales can be further transformed into parallel processing
which is beneficial for accelerating the detection process of the method. Our future work will focus on reducing the algorithm's complexity and designing a more flexible method for constructing an infrared-patch image.
Achanta R, Shaji A, Smith K, Lucchi A, Fua P and Susstrunk S. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11):2274-2282[DOI:10.1109/TPAMI.2012.120]
Ahmadi K and Salari E. 2016. Small dim object tracking using frequency and spatial domain information. Pattern Recognition, 58:227-234[DOI:10.1016/j.patcog.2016.04.001]
Bae T W and Sohng K I. 2010. Small target detection using bilateral filter based on edge component. Journal of Infrared, Millimeter, and Terahertz Waves, 31(6):735-743[DOI:10.1007/s10762-010-9633-0]
Bae T W, Zhang F and Kweon I S. 2012. Edge directional 2D LMS filter for infrared small target detection. Infrared Physics&Technology, 55(1):137-145[DOI:10.1016/j.infrared.2011.10.006]
Bai X and Zhou F. 2010. Analysis of new top-hat transformation and the application for infrared dim small target detection. Pattern Recognition, 43(6):2145-2156[DOI:10.1016/j.patcog.2009.12.023]
Bi Y, Bai X, Jin T and Guo S. 2017. Multiple feature analysis for infrared small target detection. IEEE Geoscience and Remote Sensing Letters, 14(8):1333-1337[DOI:10.1109/LGRS.2017.2711047]
Candès E J, Li X, Ma Y and Wright J. 2011. Robust principal component analysis? Journal of the ACM, 58(3):11[DOI:10.1145/1970392.1970395]
Cao Y, Yang J and Liu R M. 2009. Detecting infrared small target by using TDLMS filter based on neighborhood analysis. Journal of Infrared and Millimeter Waves, 28(3):235-240
曹原, 杨杰, 刘瑞明. 2009.基于邻域分析TDLMS滤波器的红外小目标检测.红外与毫米波学报, 28(3):235-240)[DOI:10.3321/j.issn:1001-9014.2009.03.017]
Chen C L P, Li H, Wei Y, Xia T and Tang Y Y. 2014. A local contrast method for small infrared target detection. IEEE Transactions on Geoscience and RemoteSensing, 52(1):574-581[DOI:10.1109/TGRS.2013.2242477]
Chen Y and Xin Y. 2016. An efficient infrared small target detection method based on visual contrast mechanism. IEEE Geoscience and Remote Sensing Letters, 13(7):962-966[DOI:10.1109/LGRS.2016.2556218]
Chi J N, Zhang Z H, Wang D S and Wang Z L. 2007. Application of anti-symmetrical biorthogonal wavelet in infrared image small targets detection. Journal of Astronautics, 28(5):1253-1257, 1272
迟健男, 张朝晖, 王东署, 王志良. 2007.反对称双正交小波在红外图像小目标检测中的应用.宇航学报, 28(5):1253-1257, 1272)[DOI:10.3321/j.issn:1000-1328.2007.05.035]
Dai Y M and Wu Y Q. 2017. Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(8):3752-3767[DOI:10.1109/JSTARS.2017.2700023]
Dai Y M, Wu Y Q and Song Y. 2016. Infrared small target and background separation via column-wise weighted robust principal component analysis. Infrared Physics&Technology, 77:421-430[DOI:10.1016/j.infrared.2016.06.021]
Dai Y M, Wu Y Q, Song Y and Guo J. 2017. Non-negative infrared patch-image model:robust target-background separation via partial sum minimization of singular values. Infrared Physics&Technology, 81:182-194[DOI:10.1016/j.infrared.2017.01.009]
Deng H, Sun X P, Liu M L, Ye C and Zhou X. 2017. Entropy-based window selection for detecting dim and small infrared targets. Pattern Recognition, 61:66-77[DOI:10.1016/j.patcog.2016.07.036]
Deng H, Sun X P, Liu M L, Ye C and Zhou X. 2016. Small infrared target detection based on weighted local difference measure. IEEE Transactions on Geoscience and Remote Sensing, 54(7):4204-4214[DOI:10.1109/TGRS.2016.2538295]
Deshpande S D, Er M H, VenkateswarluR V and Chan P. 1999. Max-Mean and Max-Median filters for detection of small targets//Proceedings of SPIE 3809, Signal and Data Processing of Small Targets 1999. Denver, CO, United States: SPIE, 74-83[ DOI:10.1117/12.364049 http://dx.doi.org/10.1117/12.364049 ]
Dong L L, Wang B, Zhao M and Xu W. 2017. Robust infrared maritime target detection based on visual attention and spatiotemporal filtering. IEEE Transactions on Geoscience and Remote Sensing, 55(5):3037-3050[DOI:10.1109/TGRS.2017.2660879]
Gao C Q, Meng D Y, Yang Y, Wang Y, Zhou X and Hauptmann A G. 2013. Infrared patch-image model for small target detection in a single image. IEEE Transactions on Image Processing, 22(12):4996-5009[DOI:10.1109/TIP.2013.2281420]
Gao C Q, Wang L, Xiao Y X, Zhao Q and Meng D. 2018. Infrared small-dim target detection based on Markov random field guided noise modeling. Pattern Recognition, 76:463-475[DOI:10.1016/j.patcog.2017.11.016]
Guo J, Wu Y Q and Dai Y M. 2018a. Small target detection based on reweighted infrared patch-image model. IET Image Processing, 12(1):70-79[DOI:10.1049/iet-ipr.2017.0353]
Guo Y W, Jiao L C, Wang S, Wang S, Liu F and Hua W. 2018b. Fuzzy superpixels for polarimetric SAR images classification. IEEE Transactions on Fuzzy Systems, 26(5):2846-2860[DOI:10.1109/TFUZZ.2018.2814591]
Hadhoud M M and Thomas D W. 1988. The two-dimensional adaptive LMS (TDLMS) algorithm. IEEE Transactions on Circuits and Systems, 35(5):485-494[DOI:10.1109/31.1775]
Han J H, Liang K, Zhou B, Zhu X, Zhao J and Zhao L. 2018. Infrared small target detection utilizing the multiscale relative local contrast measure. IEEE Geoscience and Remote Sensing Letters, 15(4):612-616[DOI:10.1109/LGRS.2018.2790909]
Han J H, Ma Y, Huang J, Mei X and Ma J. 2016. An infrared small target detecting algorithm based on human visual system. IEEE Geoscience and Remote Sensing Letters, 13(3):452-456[DOI:10.1109/LGRS.2016.2519144]
Han J H, Ma Y, Zhou B, Fan F, Liang K and Fang Y. 2014. A robust infrared small target detection algorithm based on human visual system. IEEE Geoscience and Remote Sensing Letters, 11(12):2168-2172[DOI:10.1109/LGRS.2014.2323236]
He Y J, Li M, Zhang J L and An Q. 2015. Small infrared target detection based on low-rank and sparse representation. Infrared Physics&Technology, 68:98-109[DOI:10.1016/j.infrared.2014.10.022]
Kim S and Lee J. 2012. Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track. Pattern Recognition, 45(1):393-406[DOI:10.1016/j.patcog.2011.06.009]
Kim S, YangY, Lee J and Park Y. 2009. Small target detection utilizing robust methods of the human visual system for IRST. Journal of Infrared, Millimeter, and Terahertz Waves, 30(9):994-1011[DOI:10.1007/s10762-009-9518-2]
Li T, Liu Z, Ran L and Rong X. 2018. Target detection by exploiting superpixel-level statistical dissimilarity for SAR imagery. IEEE Geoscience and Remote Sensing Letters, 15(4):562-566[DOI:10.1109/LGRS.2018.2805714]
Li Y S and Zhang Y J. 2018. Robust infrared small target detection using local steering kernel reconstruction. Pattern Recognition, 77:113-125[DOI:10.1016/j.patcog.2017.12.012]
Li Z Z, Chen J, Hou Q, Fu H X, Dai Z, Jin G, Li R Z and Liu C J. 2014. Sparse representation for infrared dim target detection via a discriminative over-complete dictionary learned online. Sensors, 14(6):9451-9470[DOI:10.3390/s140609451]
Liu D P, Cao L, Li ZZ, Liu T and Che P. 2018a. Infrared small target detection based on flux density and direction diversity in gradient vector field. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7):2528-2554[DOI:10.1109/JSTARS.2018.2828317]
Liu Y J, Yu M J, Li B and He Y. 2018b. Intrinsic manifold SLIC:A simple and efficient method for computing content-sensitive superpixels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3):653-666[DOI:10.1109/TPAMI.2017.2686857]
Lu R T, Huang X Sand Xu W Y. 2013. Method of infrared small target detection based on Contourlet transform and Facet model. Infrared and Laser Engineering, 42(8):2281-2287
卢瑞涛, 黄新生, 徐婉莹. 2013.基于Contourlet变换和Facet模型的红外小目标检测方法.红外与激光工程, 42(8):2281-2287)[DOI:10.3969/j.issn.1007-2276.2013.08.061]
Niu W L, Zheng W, Yang Z, Wu Y, Vagvolgyi B and Liu B. 2018. Moving point target detection based on higher order statistics in very low SNR. IEEE Geoscience and Remote Sensing Letters, 15(2):217-221[DOI:10.1109/LGRS.2017.2781229]
Qi S X, Ma J, Tao C, Yang C and Tian J. 2013. A robust directional saliency-based method for infrared small-target detection under various complex backgrounds. IEEE Geoscience and Remote Sensing Letters, 10(3):495-499[DOI:10.1109/LGRS.2012.2211094]
Rivest J F and Fortin R. 1996. Detection of dim targets in digital infrared imagery by morphological image processing. Optical Engineering, 35(7):1886-1893[DOI:10.1117/1.600620]
Shi Y F, Wei Y T, Yao H, Pan D and Xiao G. 2018. High-boost-based multiscale local contrast measure for infrared small target detection. IEEE Geoscience and Remote Sensing Letters, 15(1):33-37[DOI:10.1109/LGRS.2017.2772030]
Wan M J, Gu G H, Qian W X, Ren K and Chen Q. 2016. Robust infrared small target detection via non-negativity constraint-based sparse representation. Applied Optics, 55(27):7604-7612[DOI:10.1364/ao.55.007604]
Wang C Y and Qin S Y. 2015. Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis. Infrared Physics&Technology, 69:123-135[DOI:10.1016/j.infrared.2015.01.017]
Wang X, Lv G F and Xu L Z. 2012. Infrared dim target detection based on visual attention. Infrared Physics&Technology, 55(6):513-521[DOI:10.1016/j.infrared.2012.08.004]
Wang X Y, Peng Z M, Kong D H and He Y. 2017a. Infrared dim and small target detection based on stable multisubspace learning in heterogeneous scene. IEEE Transactions on Geoscience and Remote Sensing, 55(10):5481-5493[DOI:10.1109/TGRS.2017.2709250]
Wang X Y, Peng Z M, Kong D H, Zhang P and He Y. 2017b. Infrared dim target detection based on total variation regularization and principal component pursuit. Image and Vision Computing, 63:1-9[DOI:10.1016/j.imavis.2017.04.002]
Wang X, Shen S Q, Ning C, Xu M and Yan X. 2015. A sparse representation-based method for infrared dim target detection under sea-sky background. Infrared Physics&Technology, 71:347-355[DOI:10.1016/j.infrared.2015.05.014]
Wei Y T, You X E and Li H. 2016. Multiscale patch-based contrast measure for small infrared target detection. Pattern Recognition, 58:216-226[DOI:10.1016/j.patcog.2016.04.002]
Wu Y Q, Ji S X and Yin D Y. 2011a. Infrared small target detection based on NMF, ICA and complex Contourlettransform. Journal of Astronautics, 32(8):1833-1839
吴一全, 纪守新, 尹丹艳. 2011a.基于NMF、ICA和复Contourlet变换的红外小目标检测.宇航学报, 32(8):1833-1839)[DOI:10.3873/j.issn.1000-1328.2011.08.027]
Wu Y Q, Ji S X and Zhan B C. 2011b. Infrared dim target detection based on nonsubsampledContourlet transform and independent component analysis. ActaOpticaSinica, 31(5):0510002
吴一全, 纪守新, 占必超. 2011b.基于无下采样Contourlet变换和独立分量分析的红外弱小目标检测.光学学报, 31(5):0510002)[DOI:10.3788/AOS201131.0510002]
Wu Y Q, Luo Z J and Wu W Y. 2009. A method of small target detection in infrared image based on non-subsampled Contourlettransform. Journal of Image and Graphics, 14(3):477-481
吴一全, 罗子娟, 吴文怡. 2009.基于NSCT的红外图像小目标检测技术.中国图象图形学报, 14(3):477-481)[DOI:10.11834/jig.20090316]
Wu Y Q, Yin D Y and Ji S X. 2010. Detection of small infrared target based on dual-tree complex wavelet transform and SVR. Chinese Journal of Scientific Instrument, 31(8):1834-1839
吴一全, 尹丹艳, 纪守新. 2010.基于双树复数小波和SVR的红外小目标检测.仪器仪表学报, 31(8):1834-1839)[DOI:10.19650/j.cnki.cjsi.2010.08.025]
Zhang L D, Peng L B, Zhang T F, Cao S and Peng Z. 2018. Infrared small target detection via non-convex rank approximation minimization joint $${l_{2,1}}$$ norm. Remote Sensing, 10(11):1821-1847[DOI:10.3390/rs10111821]
Zhao J J, Tang Z Y, Yang J, Liu E Q and Zhou Y. 2011. Infrared small target detection based on image sparse representation. Journal of Infrared and Millimeter Waves, 30(2):156-161, 166
赵佳佳, 唐峥远, 杨杰, 刘尔琦, 周越. 2011.基于图像稀疏表示的红外小目标检测算法.红外与毫米波学报, 30(2):156-161, 166
Zhao Y, Pan H B, Du C P, Peng S and Zheng Y. 2014. Bilateral two-dimensional least mean square filter for infrared small target detection. Infrared Physics&Technology, 65:17-23[DOI:10.1016/j.infrared.2014.03.006]
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