Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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A Study on Vehicle Target Classification Method Using Both Shape and Local Features with Segmentation Reliability

표적분할 신뢰도 값 기반의 형태특징과 지역특징을 이용한 차량표적 분류기법 연구

  • Yang, DongWon (The 5th Research and Development Institute, Agency for Defense Development) ;
  • Lee, Yonghun (The 5th Research and Development Institute, Agency for Defense Development) ;
  • Kwak, Dongmin (The 5th Research and Development Institute, Agency for Defense Development)
  • 양동원 (국방과학연구소 제5기술연구본부) ;
  • 이용헌 (국방과학연구소 제5기술연구본부) ;
  • 곽동민 (국방과학연구소 제5기술연구본부)
  • Received : 2016.05.16
  • Accepted : 2016.12.09
  • Published : 2017.02.05
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Abstract

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

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References

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