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Palabras clave – provistas por la editorial

synthetic aperture radar; despeckling; multi-scale; LSTM; sub-pixel; high-resolution remote sensing imagery; road extraction; machine learning; DenseUNet; scene classification; lifting scheme; convolution; CNN; image classification; deep features; hand-crafted features; Sinkhorn loss; remote sensing; text image matching; triplet networks; EfficientNets; LSTM network; convolutional neural network; water identification; water index; semantic segmentation; high-resolution remote sensing image; pixel-wise classification; result correction; conditional random field (CRF); satellite; object detection; neural networks; single-shot; deep learning; global convolution network; feature fusion; depthwise atrous convolution; high-resolution representations; ISPRS vaihingen; Landsat-8; faster region-based convolutional neural network (FRCNN); single-shot multibox detector (SSD); super-resolution; remote sensing imagery; edge enhancement; satellites; open-set domain adaptation; adversarial learning; min-max entropy; pareto ranking; SAR; Sentinel–1; Open Street Map; U–Net; desert; road; infrastructure; mapping; monitoring; deep convolutional networks; outline extraction; misalignments; nearest feature selector; hyperspectral image classification; two stream residual network; Batch Normalization; plant disease detection; precision agriculture; UAV multispectral images; orthophotos registration; 3D information; orthophotos segmentation; wildfire detection; convolutional neural networks; densenet; generative adversarial networks; CycleGAN; data augmentation; pavement markings; visibility; framework; urban forests; OUDN algorithm; object-based; high spatial resolution remote sensing; Generative Adversarial Networks; post-disaster; building damage assessment; anomaly detection; Unmanned Aerial Vehicles (UAV); xBD; feature engineering; orthophoto; unsupervised segmentation