基于偏振差分图像的海天线检测方法 -

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基于偏振差分图像的海天线检测方法

doi:10.37188/CO.2022-0181

宿德志^1,,
刘亮^2,,,
王坤^1,,
吴世永^1,,
刘陵顺^1,,
明瑞龙^1,,
宫剑^3,

1.
海军航空大学航空基础学院, 山东烟台 264001
2.
海军航空大学岸防兵学院, 山东烟台 264001
3.
中国人民解放军92337部队, 辽宁大连 116023

基金项目:国家自然科学基金（No. 61205206）

详细信息

作者简介:
宿德志（1986—），男，内蒙古扎赉特旗人，硕士，副教授，主要从事红外偏振成像、红外偏振特性等方面的研究。2008年、2010年于国防科学技术大学分别获得学士、硕士学位。E-mail：sudezhifun@163.com
刘　亮（1981—），男，湖北黄石人，博士，讲师，2005年、2010年于国防科技大学分别获得硕士、博士学位，主要从事高能技术、光电对抗技术研究。E-mail：liul513@126.com
王　坤（1985—），男，山东泰安人，硕士，讲师，主要从事非线性光学晶体、偏振特性等方面的研究。E-mail：sindy5674580@sina.com
吴世永（1981—），男，广东阳春人，硕士，副教授，主要从事力学方面的研究。E-mail：wusy81@163.com
刘陵顺（1969—），男，教授，博士，研究方向为电机与控制，E-mail：lingshunliu@sohu.com
明瑞龙（1987—），男，湖北阳新人，硕士，讲师，主要从事英语二语习得、语用学等方面的研究。E-mail：rickloong@163.com
宫　剑（1990—），男，吉林珲春人，博士，研究方向为舰船红外及偏振检测，E-mail：gongjian0811@outlook.com

中图分类号:TP391.4
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- 被引次数:0
出版历程
- 收稿日期:2022-08-17
- 录用日期:2022-11-02
- 修回日期:2022-09-06
- 网络出版日期:2022-11-19

Sea-sky-line detection method based on polarization difference images

1.
College of Basic Sciences for Aviation, Naval Aviation University, Yantai 264001, China
2.
Coast Guard College, Naval Aviation University, Yantai 264001, China
3.
No. 92337 unit of PLA, Dalian 116023, China

Funds:Supported by The National Natural Science Foundation of China (No. 61205206)

More Information

Corresponding author:liul513@126.com

摘要

摘要:
针对低对比度红外图像中海天线检测困难，且易受云层、条状波浪和海杂波等干扰因素影响的问题，提出了一种采用偏振差分图像进行海天线检测的方法。首先，利用偏振差分方法增强海面区域的局部对比度和海天线的信噪比；其次，对偏振差分图像采用大尺度的局部对比度累加方法确定海天线区域；最后，在海天线区域中采用梯度显著性及多项式拟合方法完成小尺度的海天线精确检测。该方法将偏振度、偏振角等多维信息融入海天线检测，并采用了大尺度与小尺度相结合的检测方法，能够有效克服云层、条状波浪和海杂波等因素的干扰。实验结果表明该算法的海天线检测准确率为98.5%，平均耗时16 ms，能够实现快速、准确的海天线检测，具有较强的场景适用性。
- 偏振差分/
- 海天线检测/
- 低对比度/
- 梯度显著性/
- 红外图像
Abstract:
Aiming at the problem of sea-sky-line detection in low-contrast infrared images being difficult and easily affected by interference factors such as clouds, strip waves and sea clutter, we propose a method of using polarization difference images for sea-sky-line detection. Firstly, Polarization Difference Imaging (PDI) is used to enhance the local contrast of the sea surface area and the Signal-to-Noise Ratio (SNR) of the sea-sky-line. A large-scale local contrast accumulation method of the polarization difference images is then used to determine the sea-sky-line area. Finally, the accurate detection of a small-scale sea-sky-line is completed by combining the gradient significance and polynomial fitting in the sea-sky-line area. Overall, the methodology integrates multi-dimensional information such as the Degree of Linear Polarization (DOLP) and the Angle of Polarization (AOP) for sea-sky-line detection, and combines large-scale and small-scale detection, which can effectively overcome interference of factors such as clouds, strip waves and sea clutter. The experimental results show that the accuracy of this algorithm for sea-sky-line detection is 98.5%, and the average time consumed is 16 ms. The experimental results indicate that the proposed algorithm can realize fast and accurate sea-sky-line detection so it has wide applicability in different scenes.
- polarization difference/
- sea-sky-line detection/
- low contrast/
- gradient saliency/
- infrared imaging

HTML全文

图 1偏振光的分解。（a）部分偏振光强度分布；（b）完全偏振光强度分布；（c）偏振差分强度分布

Figure 1.Decomposition of polarized light. (a) Distribution of partially polarized light intensity; (b) distribution of completely polarized light intensity; (c) distribution of polarization difference light intensity

下载: 全尺寸图片幻灯片

图 2海面长波红外辐射偏振成像原理（图中 $\phi $ 为滚动角， $\varphi $ 为俯仰角， $s,p$ 分别表示 $s$ 偏振和 $p$ 偏振， ${X_c}$ 为相机水平轴）

Figure 2.Polarization imaging principle of sea surface radiation in long wave infrared band ( $\phi $ and $\varphi $ are the roll angle and pitch angle respectively; $s$ and $p$ are represent the s-polarized and p-polarized components respectively; ${X_c}$ represents the horizontal axis of the camera)

下载: 全尺寸图片幻灯片

图 3(a)强度图像与(b)差分图像结果对比(从左至右依次为场景1, 2, 3, 4)

Figure 3.Comparison of (a) intensity image and (b) difference image (From left to right are scene 1, scene2, scene 3, scene4)

下载: 全尺寸图片幻灯片

图 4红外强度图与偏振差分图特性对比

Figure 4.Comparison of infrared intensity image and polarization difference image

下载: 全尺寸图片幻灯片

图 5几种方法对存在不同干扰因素的海天线区域检测结果

Figure 5.Determination results of sea-sky-line area by different algorithms under different interference foctors

下载: 全尺寸图片幻灯片

图 6本文方法低对比度场景海天线检测结果

Figure 6.The sea-sky-line detection results by proposed algorithm in low-contrast scenes

下载: 全尺寸图片幻灯片

图 7不同场景海天线检测结果

Figure 7.The detection results of sea-sky-line in different scenes

下载: 全尺寸图片幻灯片

表 1红外强度图像与偏振差分图像的LC和SNR

Table 1.The LCs and SNRs of infrared intensity image and polarization difference image

评价指标	图像类型	场景1	场景2	场景3	场景4
LC	强度图像	0.04	0.02	0.02	0.04
LC	差分图像	0.33	0.32	0.28	0.29
SNR	强度图像	0.49	0.72	0.27	0.38
SNR	差分图像	1.03	1.46	0.96	0.95

下载: 导出CSV

表 2不同方法性能对比

Table 2.Performance comparison results by different methods

指标	霍夫变换	梯度方法+ 多项式拟合	累加方法+ 多项式拟合	本文方法
准确率(%)	36.9	87.7	95.4	98.5(128组)
平均耗时（ms）	91	102	112	16

下载: 导出CSV

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