공간 영역 및 주파수 영역 기반 SAR 표적 식별 성능 비교 (Comparison of Spatial- and Frequency-Domain-Based SAR Target Classification Performance)

서지정보

· 발행기관 : 한국전자파학회
· 수록지 정보 : 한국전자파학회 논문지 / 36권 / 5호 / 528 ~ 534페이지
· 저자명 : 채승병, 강민석

초록

본연구는합성개구레이다(SAR, synthetic aperture radar) 표적 식별에서 공간 영역과 주파수 영역의 SAR 영상에 대한분류성능을비교한다. 주파수영역의영상은공간영역의영상과푸리에변환을통해일대일로대응하며, 표적의고유한형상정보가주파수성분에도보존되므로이를활용한분류가가능하다. SAR 표적식별에는합성곱신경망모델을사용하였으며, MSTAR(moving and stationary target acquisition and recognition) 데이터셋을 활용하였다. 실험에서는 충분한수량의 학습데이터를사용할경우공간영역과주파수영역에서의성능차이는미미하였다. 반면, 제한된양의학습데이터를사용할경우주파수영역의데이터셋을활용할때의분류정확도가높게나타났다. 이를통해SAR 표적식별에 있어서공간영역뿐만아니라주파수영역에대해서도효과적인분류성능을보임을확인하였다.

영어초록

This study compares the classification performance of spatial- and frequency-domain-based synthetic aperture radar (SAR) images for target classification. In general, the image in the frequency domain corresponds one-to-one to the image in the spatial domain through Fourier transform; thus, it can be utilized for target classification because the unique information of the target is preserved in the frequency component. A convolutional neural network model was used for SAR target classification, and the moving and stationary target acquisition and Recognition dataset was utilized. In the experiments using a sufficient amount of training data, there was no significant difference in the performance of SAR target classification based on the spatial and frequency domains. However, when a limited amount of training data was used, the classification accuracy of the frequency dataset was higher than that of the spatial-domain dataset. Thus, effective classification performance was shown not only in the spatial domain but also in the frequency domain for SAR target classification.

참고자료

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