Directional support value of Gaussian transformation for infrared small target detection.
Directional support value of Gaussian transformation for infrared small target detection.
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DOI：
10.1364/ao.54.002255
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发表时间：
2015-03
期刊：
影响因子：
1.9
通讯作者：
Changcai Yang;Jiayi Ma;Shengxiang Qi;J. Tian;Sheng Zheng;Xin Tian
中科院分区：
文献类型：
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作者：
Changcai Yang;Jiayi Ma;Shengxiang Qi;J. Tian;Sheng Zheng;Xin Tian
稳健的小目标检测是红外搜索跟踪系统用于自卫或攻击的关键技术之一。本文提出了一种单幅红外图像中小目标检测的稳健解决方案。该方法的核心思想是利用高斯变换的方向支持值(DSVoGT)增强目标，并利用其提供的多尺度表示来降低虚警率。通过将原始图像与加权映射最小二乘支持向量机(LS-SVM)得到的方向支持值滤波器进行卷积，将原始图像分解成不同方向的子带。在子带图像的基础上，构造高斯矩阵的支撑值，并将该矩阵的迹定义为目标度量。为了在抑制背景杂波的同时增强目标信号，计算了相应的目标测量的多尺度相关性。在实际红外图像上验证了该方法的优越性，并与标准检测方法的检测结果进行了比较，这些检测方法包括TOP-HAT滤波、最大均值滤波、最大中值滤波、最小高斯局部拉普拉斯(LOG)滤波以及最小二乘支持向量机。对各种杂波背景图像的实验结果表明，该方法的检测性能优于其他检测方法。
Robust small target detection is one of the key techniques in IR search and tracking systems for self-defense or attacks. In this paper we present a robust solution for small target detection in a single IR image. The key ideas of the proposed method are to use the directional support value of Gaussian transform (DSVoGT) to enhance the targets, and use the multiscale representation provided by DSVoGT to reduce the false alarm rate. The original image is decomposed into sub-bands in different orientations by convolving the image with the directional support value filters, which are deduced from the weighted mapped least-squares-support vector machines (LS-SVMs). Based on the sub-band images, a support value of Gaussian matrix is constructed, and the trace of this matrix is then defined as the target measure. The corresponding multiscale correlations of the target measures are computed for enhancing target signal while suppressing the background clutter. We demonstrate the advantages of the proposed method on real IR images and compare the results against those obtained from standard detection approaches, including the top-hat filter, max-mean filter, max-median filter, min-local-Laplacian of Gaussian (LoG) filter, as well as LS-SVM. The experimental results on various cluttered background images show that the proposed method outperforms other detectors.