TY  - GEN
T1  - Synthetic Aperture Radar (SAR) Meets Deep Learning
A2  - Zhang, Tianwen
LA  - eng
PB  - MDPI - Multidisciplinary Digital Publishing Institute
YR  - 2023
UL  - https://ebooks.jgu.edu.in/Record/doab-20.500.12854-96774
AB  - 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.
SN  - 9783036563824
SN  - 9783036563831
KW  - heterogeneous transformation
KW  - SAR image
KW  - optical image
KW  - conditional generative adversarial nets (CGANs)
KW  - self-supervised
KW  - synthetic aperture radar (SAR)
KW  - despeckling
KW  - enhanced U-Net
KW  - video synthetic aperture radar (Video-SAR)
KW  - moving target tracking
KW  - guided anchor Siamese network (GASN)
KW  - interferometric synthetic aperture radar
KW  - deep convolutional neural network
KW  - phase unwrapping
KW  - unsupervised change detection
KW  - polarimetric synthetic aperture radar (PolSAR)
KW  - UAVSAR
KW  - multi-scale shallow block
KW  - multi-scale residual block
KW  - synthetic aperture radar
KW  - image registration
KW  - transformer
KW  - deep learning
KW  - SAR target detection
KW  - multiscale learning
KW  - ship detection
KW  - SAR ship detection
KW  - position-enhanced attention
KW  - lightweight backbone
KW  - image augmentation
KW  - building extraction
KW  - SAR
KW  - semantic segmentation
KW  - SAR dataset
KW  - single-stage detector
KW  - two-stage detector
KW  - anchor free
KW  - train from scratch
KW  - oriented bounding box
KW  - multi-scale detection
KW  - computer vision
KW  - low-grade road extraction
KW  - remote sensing
KW  - image segmentation
KW  - optical images
KW  - scene classification
KW  - on-board
KW  - lightweight self-supervised algorithm
KW  - synthetic aperture radar (SAR) image
KW  - arbitrary-oriented ship detection
KW  - differentiable rotational IoU algorithm
KW  - triangle distance IoU loss
KW  - attention-weighted feature pyramid network
KW  - multiple skip-scale connections
KW  - attention-weighted feature fusion
KW  - Rotated-SARShip dataset (RSSD)
KW  - object classification
KW  - radar image reconstruction
KW  - convolutional neural networks
KW  - ResNet18
KW  - GBSAR
KW  - Omega-K algorithm
KW  - n/a
KW  - thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
KW  - thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
ER  -