Artificial Intelligence-Based Learning Approaches for Remote Sensing

• Andere auteurs: Jeon, Gwanggil (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Onderwerpen: Technology: general issues; History of engineering & technology; Environmental science, engineering & technology; pine wilt disease dataset; GIS application visualization; test-time augmentation; object detection; hard negative mining; video synthetic aperture radar (SAR); moving target; shadow detection; deep learning; false alarms; missed detections; synthetic aperture radar (SAR); on-board; ship detection; YOLOv5; lightweight detector; remote sensing image; spectral domain translation; generative adversarial network; paired translation; synthetic aperture radar; ship instance segmentation; global context modeling; boundary-aware box prediction; land-use and land-cover; built-up expansion; probability modelling; landscape fragmentation; machine learning; support vector machine; frequency ratio; fuzzy logic; artificial intelligence; remote sensing; interferometric phase filtering; sparse regularization (SR); deep learning (DL); neural convolutional network (CNN); semantic segmentation; open data; building extraction; unet; deeplab; classifying-inversion method; AIS; atmospheric duct; ship detection and classification; rotated bounding box; attention; feature alignment; weather nowcasting; ResNeXt; radar data; spectral-spatial interaction network; spectral-spatial attention; pansharpening; UAV visual navigation; Siamese network; multi-order feature; MIoU; imbalanced data classification; data over-sampling; graph convolutional network; semi-supervised learning; troposcatter; tropospheric turbulence; intercity co-channel interference; concrete bridge; visual inspection; defect; deep convolutional neural network; transfer learning; interpretation techniques; weakly supervised semantic segmentation; n/a
• Online toegang: DOAB: download the publication; DOAB: description of the publication

The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments...