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Palabras clave – provistas por la editorial

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