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자동표적인식 성능 향상을 위한 전자광학/적외선 영상 잡음 판별 기법
조준후(Jun Hoo Cho),강창호(Chang Ho Kang),박찬국(Chan Gook Park) 제어로봇시스템학회 2017 제어·로봇·시스템학회 논문지 Vol.23 No.2
We propose a method to identify image noise type for an automatic target recognition system. In previous studies, kurtosis and skewness of image noise have been considered during identification. However, these two features vary according to each image, whereby the identification accuracy is not convincing. In order to maintain the performance of noise identification according to various images and intensities, we carried out a logistic regression analysis and designed a model-based image noise identification method using random sample consensus (RANSAC). It was confirmed that the proposed algorithm identifies 3 types of image noise according to 50 different images and 4 different noise levels.
합성곱 인공 신경망을 적용한 SAR 영상 표적 인식 알고리즘
조준후(Jun Hoo Cho),강창호(Chang Ho Kang),박찬국(Chan Gook Park) 제어로봇시스템학회 2017 제어·로봇·시스템학회 논문지 Vol.23 No.8
In this paper, we have designed an SAR automatic target recognition (SAR ATR) algorithm using the convolutional neural network (CNN), which has excellent image recognition performance. Previous SAR ATR methods are difficult to implement, because they include additional preprocessing processes or need prior SAR image information. To address these issues, we propose a CNN structure that is specialized for SAR image classification by modifying the structure of VGGNet. It is confirmed by simulation that the classification accuracy of the proposed method on the MSTAR SAR dataset is increased by 1–2% compared with the conventional VGGNet. Moreover, the classification performance is further improved when the train data is much smaller than the test data.