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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。
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.
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Key words:
- polarization difference /
- sea-sky-line detection /
- low contrast /
- gradient saliency /
- infrared imaging
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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
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)
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)
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
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
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
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 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 差分图像 1.03 1.46 0.96 0.95 
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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
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