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      Siamese 네트워크 기반 SAR 표적영상 간 유사도 분석 = Similarity Analysis Between SAR Target Images Based on Siamese Network

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      https://www.riss.kr/link?id=A108310575

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      다국어 초록 (Multilingual Abstract)

      Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to e...

      Different from the field of electro-optical(EO) image analysis, there has been less interest in similarity metrics between synthetic aperture radar(SAR) target images. A reliable and objective similarity analysis for SAR target images is expected to enable the verification of the SAR measurement process or provide the guidelines of target CAD modeling that can be used for simulating realistic SAR target images. For this purpose, this paper presents a similarity analysis method based on the siamese network that quantifies the subjective assessment through the distance learning of similar and dissimilar SAR target image pairs. The proposed method is applied to MSTAR SAR target images of slightly different depression angles and the resultant metrics are compared and analyzed with qualitative evaluation. Since the image similarity is somewhat related to recognition performance, the capacity of the proposed method for target recognition is further checked experimentally with the confusion matrix.

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      참고문헌 (Reference) 논문관계도

      1 박지훈 ; 임호, "산란점 정보를 이용한 표적 SAR 영상 간 유사도 평가기법" 한국군사과학기술학회 22 (22): 735-744, 2019

      2 A. O. Knapskog, "Target Recognition of Ships in Harbour Based on Simulated SAR Images Produced with MOCEM Software" 608-611, 2014

      3 S. Chen, "Target Classification Using the Deep Convolutional Networks for SAR Images" 54 (54): 4806-4817, 2016

      4 G. Koch, "Siamese Neural Networks for One-Shot Image Recognition" 37 : 2015

      5 J. Pei, "SAR Automatic Target Recognition Based on Multiview Deep Learning Framework" 56 (56): 2196-2210, 2018

      6 A. Horé, "Image Quality Metrics: PSNR vs SSIM" 2010

      7 Z. Wang, "Image Quality Assessment: From Error Visibility to Structural Similarity" 13 (13): 600-612, 2004

      8 L. Hughes, "Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN" 15 (15): 784-788, 2018

      9 R. C. Daudt, "Fully Convolutional Siamese Networks for Change Detection" 4063-4067, 2018

      10 N. Merkle, "Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images" 9 : 586-, 2017

      1 박지훈 ; 임호, "산란점 정보를 이용한 표적 SAR 영상 간 유사도 평가기법" 한국군사과학기술학회 22 (22): 735-744, 2019

      2 A. O. Knapskog, "Target Recognition of Ships in Harbour Based on Simulated SAR Images Produced with MOCEM Software" 608-611, 2014

      3 S. Chen, "Target Classification Using the Deep Convolutional Networks for SAR Images" 54 (54): 4806-4817, 2016

      4 G. Koch, "Siamese Neural Networks for One-Shot Image Recognition" 37 : 2015

      5 J. Pei, "SAR Automatic Target Recognition Based on Multiview Deep Learning Framework" 56 (56): 2196-2210, 2018

      6 A. Horé, "Image Quality Metrics: PSNR vs SSIM" 2010

      7 Z. Wang, "Image Quality Assessment: From Error Visibility to Structural Similarity" 13 (13): 600-612, 2004

      8 L. Hughes, "Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN" 15 (15): 784-788, 2018

      9 R. C. Daudt, "Fully Convolutional Siamese Networks for Change Detection" 4063-4067, 2018

      10 N. Merkle, "Exploiting Deep Matching and SAR Data for the Geo-Localization Accuracy Improvement of Optical Satellite Images" 9 : 586-, 2017

      11 A. Baraldi, "An Investigation of the Textural Characteristics Associated with Gray Level Cooccurrence Matrix Statistical Parameters" 33 (33): 293-304, 1995

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