Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation With ResNet
Synthetic aperture radar (SAR) imaging has recently attracted considerable attention due to its variety of applications in both military and civilian aspects. However, a SAR image scheme can be affected by various elements that can lead to poor image reconstruction performance, especially for the ta...
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IEEE
2023-01-01
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| author | Pham The Hien Ic-Pyo Hong |
| author_facet | Pham The Hien Ic-Pyo Hong |
| author_sort | Pham The Hien |
| collection | DOAJ |
| description | Synthetic aperture radar (SAR) imaging has recently attracted considerable attention due to its variety of applications in both military and civilian aspects. However, a SAR image scheme can be affected by various elements that can lead to poor image reconstruction performance, especially for the target recognition mission; for instance, the complex environment, irregular sampling intervals, sample scarcity, imaging parameters, etc. The rapid development of deep learning currently makes it a great solution to deal with the aforementioned problems. In this paper, we propose a SAR image model based on conditional generative adversarial networks (cGAN), which combines image-to-image translation (pix2pix) and residual networks (ResNet) in order to diminish the noise and artifacts on SAR images, increase their signal-to-clutter-noise ratio (SNCR) of the images, and improve the short-range target recognition rate. Unlike conventional cGAN, we employ a ResNet-based discriminator (RbD) to effectively improve the SAR image denoising ability of the model. On the other hand, another similar discriminator is simultaneously trained to classify 14 familiar metallic object types with high accuracy and avoid the over-fitting problem. This discriminator is built by replicating the RbD one, and then we replace the last layer with the standard softmax function to classify multiple objects based on class probability outputs. The experiment results in this paper illustrate that the proposed scheme achieves higher image denoising performance and SNCR enhancement than the other conventional approaches. Besides, the target recognition rate of the proposed scheme outperforms the other common classification models. |
| format | Article |
| id | doaj-art-f155452ccfcf48e882849de4c1706a42 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-f155452ccfcf48e882849de4c1706a422025-08-20T02:43:43ZengIEEEIEEE Access2169-35362023-01-0111702037021510.1109/ACCESS.2023.329364410177151Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation With ResNetPham The Hien0https://orcid.org/0000-0003-3800-0703Ic-Pyo Hong1https://orcid.org/0000-0003-1875-5420Department of Smart Information and Technology Engineering, Kongju National University, Cheonan, South KoreaDepartment of Smart Information and Technology Engineering, Kongju National University, Cheonan, South KoreaSynthetic aperture radar (SAR) imaging has recently attracted considerable attention due to its variety of applications in both military and civilian aspects. However, a SAR image scheme can be affected by various elements that can lead to poor image reconstruction performance, especially for the target recognition mission; for instance, the complex environment, irregular sampling intervals, sample scarcity, imaging parameters, etc. The rapid development of deep learning currently makes it a great solution to deal with the aforementioned problems. In this paper, we propose a SAR image model based on conditional generative adversarial networks (cGAN), which combines image-to-image translation (pix2pix) and residual networks (ResNet) in order to diminish the noise and artifacts on SAR images, increase their signal-to-clutter-noise ratio (SNCR) of the images, and improve the short-range target recognition rate. Unlike conventional cGAN, we employ a ResNet-based discriminator (RbD) to effectively improve the SAR image denoising ability of the model. On the other hand, another similar discriminator is simultaneously trained to classify 14 familiar metallic object types with high accuracy and avoid the over-fitting problem. This discriminator is built by replicating the RbD one, and then we replace the last layer with the standard softmax function to classify multiple objects based on class probability outputs. The experiment results in this paper illustrate that the proposed scheme achieves higher image denoising performance and SNCR enhancement than the other conventional approaches. Besides, the target recognition rate of the proposed scheme outperforms the other common classification models.https://ieeexplore.ieee.org/document/10177151/Automatic target recognitionfrequency-modulated continuous wavegenerative adversarial networksmillimeter wave radarresidual networkssynthetic aperture radar |
| spellingShingle | Pham The Hien Ic-Pyo Hong Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation With ResNet IEEE Access Automatic target recognition frequency-modulated continuous wave generative adversarial networks millimeter wave radar residual networks synthetic aperture radar |
| title | Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation With ResNet |
| title_full | Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation With ResNet |
| title_fullStr | Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation With ResNet |
| title_full_unstemmed | Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation With ResNet |
| title_short | Millimeter Wave SAR Imaging Denoising and Classification by Combining Image-to-Image Translation With ResNet |
| title_sort | millimeter wave sar imaging denoising and classification by combining image to image translation with resnet |
| topic | Automatic target recognition frequency-modulated continuous wave generative adversarial networks millimeter wave radar residual networks synthetic aperture radar |
| url | https://ieeexplore.ieee.org/document/10177151/ |
| work_keys_str_mv | AT phamthehien millimeterwavesarimagingdenoisingandclassificationbycombiningimagetoimagetranslationwithresnet AT icpyohong millimeterwavesarimagingdenoisingandclassificationbycombiningimagetoimagetranslationwithresnet |