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Palabras clave – provistas por la editorial

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