Details
- Autor(en) / Beteiligte
- Titel
- Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images
- Ort / Verlag
- Basel, Switzerland: MDPI - Multidisciplinary Digital Publishing Institute
- Erscheinungsjahr
- 2021
- Link zum Volltext
- Quelle
- Alma/SFX Local Collection
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISBN: 9783036509877, 3036509860, 9783036509860, 3036509879DOI: 10.3390/books978-3-0365-0987-7Titel-ID: cdi_oapen_doabooks_76425
- Format
- –
- Schlagworte
- 3D information, adversarial learning, anomaly detection, Batch Normalization, building damage assessment, CNN, conditional random field (CRF), convolution, convolutional neural network, convolutional neural networks, CycleGAN, data augmentation, deep convolutional networks, deep features, deep learning, densenet, DenseUNet, depthwise atrous convolution, desert, despeckling, edge enhancement, EfficientNets, faster region-based convolutional neural network (FRCNN), feature engineering, feature fusion, framework, generative adversarial networks, global convolution network, hand-crafted features, high spatial resolution remote sensing, high-resolution remote sensing image, high-resolution remote sensing imagery, high-resolution representations, hyperspectral image classification, image classification, infrastructure, ISPRS vaihingen, Landsat-8, lifting scheme, LSTM, LSTM network, machine learning, mapping, min-max entropy, misalignments, monitoring, multi-scale, nearest feature selector, neural networks, object detection, object-based, Open Street Map, open-set domain adaptation, orthophoto, orthophotos registration, orthophotos segmentation, OUDN algorithm, outline extraction, pareto ranking, pavement markings, pixel-wise classification, plant disease detection, post-disaster, precision agriculture, Reference, Information and Interdisciplinary subjects, remote sensing, remote sensing imagery, Research and information: general, result correction, road, road extraction, SAR, satellite, satellites, scene classification, semantic segmentation, Sentinel–1, single-shot, single-shot multibox detector (SSD), Sinkhorn loss, sub-pixel, super-resolution, synthetic aperture radar, text image matching, thema EDItEUR, triplet networks, two stream residual network, UAV multispectral images, Unmanned Aerial Vehicles (UAV), unsupervised segmentation, urban forests, U–Net, visibility, water identification, water index, wildfire detection, xBD