LEADER 02768oam 2200397 450 001 9910647228203321 005 20231201221911.0 010 $a3-0365-6383-0 035 $a(CKB)5680000000300064 035 $a(NjHacI)995680000000300064 035 $a(EXLCZ)995680000000300064 100 $a20230323d2023 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSynthetic aperture radar (SAR) meets deep learning /$fTianwen Zhang, Tianjiao Zeng, Xiaoling Zhang, editor 210 1$a[Basel] :$cMDPI - Multidisciplinary Digital Publishing Institute,$d2023. 215 $a1 online resource (386 pages) 311 0 $a3-0365-6382-2 327 $aIntroduction -- Overview of Contribution -- Conclusions -- Author Contributions -- Data Availability Statement -- Acknowledgments -- Conflicts of Interest -- References. 330 $aThis 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. 517 $aSynthetic Aperture Radar 606 $aMarine pollution 615 0$aMarine pollution. 676 $a363.7394 702 $aZhang$b Xiaoling 702 $aZeng$b Tianjiao 702 $aZhang$b Tianwen 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910647228203321 996 $aSynthetic Aperture Radar (SAR) Meets Deep Learning$93018170 997 $aUNINA