| 基于背景建模的VideoSAR动目标阴影检测方法 |
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| 引用本文： | 王鑫,田甜,田金文.基于背景建模的VideoSAR动目标阴影检测方法[J].智能系统学报,2022,17(1):59-68. |
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| 作者姓名： | 王鑫 田甜 田金文 |
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| 作者单位： | 1. 华中科技大学 人工智能与自动化学院，湖北 武汉 430074;2. 华中科技大学 多谱信息处理技术国家级重点实验室，湖北 武汉 430074 |
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| 基金项目： | 国家自然科学基金项目(42071339)。 |
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| 摘 要： | 针对视频合成孔径雷达(video synthetic aperture radar，VideoSAR)数据进行地面运动目标检测的问题，本文提出了一种基于单高斯背景模型的VideoSAR动目标阴影检测方法。该方法使用一个时间维度的滑窗对视频序列进行处理:首先使用RED20深度神经网络模型抑制VideoSAR图像的斑点噪声，随后使用帧间配准算法快速配准窗口内的图像序列，然后对序列进行建模和差分得到窗口末帧的二值化前景，最后通过连通区域筛选和区域生长剔除虚假目标。采用美国Sandia国家实验室公布的VideoSAR视频对本文算法进行了验证，实验表明，该算法能实现对动目标阴影的准确检测。
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| 关 键 词： | 视频合成孔径雷达 动目标检测 卷积神经网络 乘性噪声 去噪 图像配准 单高斯模型 区域生长 |
Moving target shadow detection in VideoSAR based on background modeling |
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| WANG Xin,TIAN Tian,TIAN Jinwen.Moving target shadow detection in VideoSAR based on background modeling[J].CAAL Transactions on Intelligent Systems,2022,17(1):59-68. |
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| Authors: | WANG Xin TIAN Tian TIAN Jinwen |
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| Affiliation: | 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Huangzhong University of Science and Technology, Wuhan 430074, China |
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| Abstract: | Aiming at the problem of ground moving target detection based on Video Synthetic Aperture Radar (VideoSAR) data, a VideoSAR moving target shadow detection method based on single Gaussian background model is proposed in this paper, which uses a time-dimensional sliding window to process the video sequence: The RED20 deep neural network model is first used to suppress the speckle noise of VideoSAR image, and then the interframe registration algorithm is applied to quickly register the image sequence of the window. After that, the binary foreground of the last frame of the window is obtained by sequence modeling and background subtraction. Finally, false targets are eliminated by connected region screening and region growing. The proposed approach is validated on the VideoSAR video published by Sandia National Laboratory, and experimental results show that the algorithm can accurately detect the shadow of moving targets. |
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| Keywords: | VideoSAR moving target detection convolutional neural network multiplicative noise denoising image registration single Gaussian model region growing |
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