| 基于统计特征空间提取和支持向量机的极暗弱小天体检测方法 |
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| 引用本文： | 王存远,郑伟,李明涛.基于统计特征空间提取和支持向量机的极暗弱小天体检测方法[J].空间科学学报,2023,43(1):119-128. |
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| 作者姓名： | 王存远 郑伟 李明涛 |
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| 作者单位： | 中国科学院国家空间科学中心 北京 100190 |
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| 摘 要： | 小天体检测是小天体防御和预警的前提。针对小天体目标信噪比低、检测难的问题，提出了基于统计特征空间提取和支持向量机（SVM）的极暗弱小天体检测方法。区别于传统方法基于时间或空间上目标的能量和背景噪声能量的瞬时能量差别或是瞬时能量差别的累积，对目标进行检测。该方法不依赖目标能量大小，提取运动目标穿过背景时对稳定性产生的扰动来反演运动目标。将输入的图像序列转化为单像元时序信号，划分时序窗口提取统计特征，关联形成统计特征空间，利用更高维度的变化特性检测目标变化。通过SVM将暗弱小天体检测问题转化为目标与背景的二分类问题，避开了较难解决的阈值分割问题同时具有更好的泛化性能。利用真实数据与其他经典方法进行对比分析，使得分类准确率提高4.02%。该方法能够适应更低的信噪比，在极低信噪比下仍表现出稳定的检测性能。
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| 关 键 词： | 运动目标检测 低信噪比 小天体 特征空间 支持向量机 |
| 收稿时间： | 2021-12-30 |
Dim Small Object Detection Method Based on Statistical Feature Space Extraction and SVM |
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| Affiliation: | National Space Science Center, Chinese Academy of Sciences, Beijing 100190University of Chinese Academy of Sciences, Beijing 100049 |
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| Abstract: | Small object detection is the premise of small object defense and early warning. Aiming at the problems of low signal-to-noise ratio and difficult detection of small object targets, a very dark and weak small object detection method based on statistical feature space extraction and SVM is proposed. It is different from traditional methods to detect targets based on the instantaneous energy difference or the accumulation of instantaneous energy difference between target energy and background noise energy in time or space. This method does not directly depend on the target energy, but extracts the disturbance to the stability when the moving target passes through the background to retrieve the moving target. The input image sequence is transformed into a single pixel timing signal, the timing window is divided, the statistical features are extracted, the statistical feature space is formed by correlation, and the target change is detected by using the change characteristics of higher dimensions. The method is implemented by Support Vector Machine (SVM) transforms the dim small object detection problem into the binary classification problem of target and background, so that the method avoids the thorny threshold segmentation problem and has better generalization performance. Through the comparative analysis between real data and other classical methods, the classification accuracy is increased by 4.02%. This method can adapt to lower signal-to-noise ratio and still perform well at very low signal-to-noise ratio Stable detection performance. |
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