TY - BOOK ID - 145999421 TI - Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images AU - Bazi, Yakoub AU - Pasolli, Edoardo PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Research & information: general KW - synthetic aperture radar KW - despeckling KW - multi-scale KW - LSTM KW - sub-pixel KW - high-resolution remote sensing imagery KW - road extraction KW - machine learning KW - DenseUNet KW - scene classification KW - lifting scheme KW - convolution KW - CNN KW - image classification KW - deep features KW - hand-crafted features KW - Sinkhorn loss KW - remote sensing KW - text image matching KW - triplet networks KW - EfficientNets KW - LSTM network KW - convolutional neural network KW - water identification KW - water index KW - semantic segmentation KW - high-resolution remote sensing image KW - pixel-wise classification KW - result correction KW - conditional random field (CRF) KW - satellite KW - object detection KW - neural networks KW - single-shot KW - deep learning KW - global convolution network KW - feature fusion KW - depthwise atrous convolution KW - high-resolution representations KW - ISPRS vaihingen KW - Landsat-8 KW - faster region-based convolutional neural network (FRCNN) KW - single-shot multibox detector (SSD) KW - super-resolution KW - remote sensing imagery KW - edge enhancement KW - satellites KW - open-set domain adaptation KW - adversarial learning KW - min-max entropy KW - pareto ranking KW - SAR KW - Sentinel–1 KW - Open Street Map KW - U–Net KW - desert KW - road KW - infrastructure KW - mapping KW - monitoring KW - deep convolutional networks KW - outline extraction KW - misalignments KW - nearest feature selector KW - hyperspectral image classification KW - two stream residual network KW - Batch Normalization KW - plant disease detection KW - precision agriculture KW - UAV multispectral images KW - orthophotos registration KW - 3D information KW - orthophotos segmentation KW - wildfire detection KW - convolutional neural networks KW - densenet KW - generative adversarial networks KW - CycleGAN KW - data augmentation KW - pavement markings KW - visibility KW - framework KW - urban forests KW - OUDN algorithm KW - object-based KW - high spatial resolution remote sensing KW - Generative Adversarial Networks KW - post-disaster KW - building damage assessment KW - anomaly detection KW - Unmanned Aerial Vehicles (UAV) KW - xBD KW - feature engineering KW - orthophoto KW - unsupervised segmentation KW - synthetic aperture radar KW - despeckling KW - multi-scale KW - LSTM KW - sub-pixel KW - high-resolution remote sensing imagery KW - road extraction KW - machine learning KW - DenseUNet KW - scene classification KW - lifting scheme KW - convolution KW - CNN KW - image classification KW - deep features KW - hand-crafted features KW - Sinkhorn loss KW - remote sensing KW - text image matching KW - triplet networks KW - EfficientNets KW - LSTM network KW - convolutional neural network KW - water identification KW - water index KW - semantic segmentation KW - high-resolution remote sensing image KW - pixel-wise classification KW - result correction KW - conditional random field (CRF) KW - satellite KW - object detection KW - neural networks KW - single-shot KW - deep learning KW - global convolution network KW - feature fusion KW - depthwise atrous convolution KW - high-resolution representations KW - ISPRS vaihingen KW - Landsat-8 KW - faster region-based convolutional neural network (FRCNN) KW - single-shot multibox detector (SSD) KW - super-resolution KW - remote sensing imagery KW - edge enhancement KW - satellites KW - open-set domain adaptation KW - adversarial learning KW - min-max entropy KW - pareto ranking KW - SAR KW - Sentinel–1 KW - Open Street Map KW - U–Net KW - desert KW - road KW - infrastructure KW - mapping KW - monitoring KW - deep convolutional networks KW - outline extraction KW - misalignments KW - nearest feature selector KW - hyperspectral image classification KW - two stream residual network KW - Batch Normalization KW - plant disease detection KW - precision agriculture KW - UAV multispectral images KW - orthophotos registration KW - 3D information KW - orthophotos segmentation KW - wildfire detection KW - convolutional neural networks KW - densenet KW - generative adversarial networks KW - CycleGAN KW - data augmentation KW - pavement markings KW - visibility KW - framework KW - urban forests KW - OUDN algorithm KW - object-based KW - high spatial resolution remote sensing KW - Generative Adversarial Networks KW - post-disaster KW - building damage assessment KW - anomaly detection KW - Unmanned Aerial Vehicles (UAV) KW - xBD KW - feature engineering KW - orthophoto KW - unsupervised segmentation UR - https://www.unicat.be/uniCat?func=search&query=sysid:145999421 AB - The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching. ER -