Volume 43 Issue 1
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Article Navigation >  Chinese Journal of Space Science  > 2023  >  43(1): 119-128 Open Access
WANG Cunyuan, ZHENG Wei, LI Mingtao. Dim Small Object Detection Method Based on Statistical Feature Space Extraction and SVM (in Chinese). Chinese Journal of Space Science, 2023, 43(1): 119-128 doi: 10.11728/cjss2023.01.211231136
Citation: WANG Cunyuan, ZHENG Wei, LI Mingtao. Dim Small Object Detection Method Based on Statistical Feature Space Extraction and SVM (in Chinese). Chinese Journal of Space Science, 2023, 43(1): 119-128 doi: 10.11728/cjss2023.01.211231136
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Dim Small Object Detection Method Based on Statistical Feature Space Extraction and SVM

doi: 10.11728/cjss2023.01.211231136 cstr: 32142.14.cjss2023.01.211231136

  • National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • University of Chinese Academy of Sciences, Beijing 100049

  • Received Date: 2021-12-30
  • Accepted Date: 2022-03-22
  • Rev Recd Date: 2022-10-16
    • Available Online: 2023-02-14
    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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