Synthetic Aperture Radar (SAR) Meets Deep Learning
This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whos...
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| Aineistotyyppi: | Online |
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| Kieli: | englanti |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Aiheet: | |
| Linkit: | ONIX_20230202_9783036563824_175 |
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| _version_ | 1869523353719537664 |
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| collection | Directory of Open Access Books |
| description | This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports. |
| format | Online |
| id | doab-20.500.12854ir-96774 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-967742024-04-09T23:16:09Z Synthetic Aperture Radar (SAR) Meets Deep Learning Zhang, Tianwen Zeng, Tianjiao Zhang, Xiaoling heterogeneous transformation SAR image optical image conditional generative adversarial nets (CGANs) self-supervised synthetic aperture radar (SAR) despeckling enhanced U-Net video synthetic aperture radar (Video-SAR) moving target tracking guided anchor Siamese network (GASN) interferometric synthetic aperture radar deep convolutional neural network phase unwrapping unsupervised change detection polarimetric synthetic aperture radar (PolSAR) UAVSAR multi-scale shallow block multi-scale residual block synthetic aperture radar image registration transformer deep learning SAR target detection multiscale learning ship detection SAR ship detection position-enhanced attention lightweight backbone image augmentation building extraction SAR semantic segmentation SAR dataset single-stage detector two-stage detector anchor free train from scratch oriented bounding box multi-scale detection computer vision low-grade road extraction remote sensing image segmentation optical images scene classification on-board lightweight self-supervised algorithm synthetic aperture radar (SAR) image arbitrary-oriented ship detection differentiable rotational IoU algorithm triangle distance IoU loss attention-weighted feature pyramid network multiple skip-scale connections attention-weighted feature fusion Rotated-SARShip dataset (RSSD) object classification radar image reconstruction convolutional neural networks ResNet18 GBSAR Omega-K algorithm n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports. 2023-02-02T16:50:23Z 2023-02-02T16:50:23Z 2023 book ONIX_20230202_9783036563824_175 9783036563824 9783036563831 https://directory.doabooks.org/handle/20.500.12854/96774 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6720 https://mdpi.com/books/pdfview/book/6720 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-6383-1 10.3390/books978-3-0365-6383-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036563824 9783036563831 386 Basel open access |
| spellingShingle | heterogeneous transformation SAR image optical image conditional generative adversarial nets (CGANs) self-supervised synthetic aperture radar (SAR) despeckling enhanced U-Net video synthetic aperture radar (Video-SAR) moving target tracking guided anchor Siamese network (GASN) interferometric synthetic aperture radar deep convolutional neural network phase unwrapping unsupervised change detection polarimetric synthetic aperture radar (PolSAR) UAVSAR multi-scale shallow block multi-scale residual block synthetic aperture radar image registration transformer deep learning SAR target detection multiscale learning ship detection SAR ship detection position-enhanced attention lightweight backbone image augmentation building extraction SAR semantic segmentation SAR dataset single-stage detector two-stage detector anchor free train from scratch oriented bounding box multi-scale detection computer vision low-grade road extraction remote sensing image segmentation optical images scene classification on-board lightweight self-supervised algorithm synthetic aperture radar (SAR) image arbitrary-oriented ship detection differentiable rotational IoU algorithm triangle distance IoU loss attention-weighted feature pyramid network multiple skip-scale connections attention-weighted feature fusion Rotated-SARShip dataset (RSSD) object classification radar image reconstruction convolutional neural networks ResNet18 GBSAR Omega-K algorithm n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Synthetic Aperture Radar (SAR) Meets Deep Learning |
| title | Synthetic Aperture Radar (SAR) Meets Deep Learning |
| title_full | Synthetic Aperture Radar (SAR) Meets Deep Learning |
| title_fullStr | Synthetic Aperture Radar (SAR) Meets Deep Learning |
| title_full_unstemmed | Synthetic Aperture Radar (SAR) Meets Deep Learning |
| title_short | Synthetic Aperture Radar (SAR) Meets Deep Learning |
| title_sort | synthetic aperture radar sar meets deep learning |
| topic | heterogeneous transformation SAR image optical image conditional generative adversarial nets (CGANs) self-supervised synthetic aperture radar (SAR) despeckling enhanced U-Net video synthetic aperture radar (Video-SAR) moving target tracking guided anchor Siamese network (GASN) interferometric synthetic aperture radar deep convolutional neural network phase unwrapping unsupervised change detection polarimetric synthetic aperture radar (PolSAR) UAVSAR multi-scale shallow block multi-scale residual block synthetic aperture radar image registration transformer deep learning SAR target detection multiscale learning ship detection SAR ship detection position-enhanced attention lightweight backbone image augmentation building extraction SAR semantic segmentation SAR dataset single-stage detector two-stage detector anchor free train from scratch oriented bounding box multi-scale detection computer vision low-grade road extraction remote sensing image segmentation optical images scene classification on-board lightweight self-supervised algorithm synthetic aperture radar (SAR) image arbitrary-oriented ship detection differentiable rotational IoU algorithm triangle distance IoU loss attention-weighted feature pyramid network multiple skip-scale connections attention-weighted feature fusion Rotated-SARShip dataset (RSSD) object classification radar image reconstruction convolutional neural networks ResNet18 GBSAR Omega-K algorithm n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | heterogeneous transformation SAR image optical image conditional generative adversarial nets (CGANs) self-supervised synthetic aperture radar (SAR) despeckling enhanced U-Net video synthetic aperture radar (Video-SAR) moving target tracking guided anchor Siamese network (GASN) interferometric synthetic aperture radar deep convolutional neural network phase unwrapping unsupervised change detection polarimetric synthetic aperture radar (PolSAR) UAVSAR multi-scale shallow block multi-scale residual block synthetic aperture radar image registration transformer deep learning SAR target detection multiscale learning ship detection SAR ship detection position-enhanced attention lightweight backbone image augmentation building extraction SAR semantic segmentation SAR dataset single-stage detector two-stage detector anchor free train from scratch oriented bounding box multi-scale detection computer vision low-grade road extraction remote sensing image segmentation optical images scene classification on-board lightweight self-supervised algorithm synthetic aperture radar (SAR) image arbitrary-oriented ship detection differentiable rotational IoU algorithm triangle distance IoU loss attention-weighted feature pyramid network multiple skip-scale connections attention-weighted feature fusion Rotated-SARShip dataset (RSSD) object classification radar image reconstruction convolutional neural networks ResNet18 GBSAR Omega-K algorithm n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20230202_9783036563824_175 |