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220822 ||| eng |
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|a books978-3-0365-0987-7
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|a 9783036509877
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|a 9783036509860
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| 100 |
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|a Bazi, Yakoub
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| 245 |
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|a Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (438 p.)
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| 653 |
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|a remote sensing imagery
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|a machine learning
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|a DenseUNet
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|a min-max entropy
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|a U-Net
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|a 3D information
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|a feature fusion
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|a building damage assessment
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|a data augmentation
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|a deep learning
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|a super-resolution
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|a desert
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|a Landsat-8
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|a convolution
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|a neural networks
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|a wildfire detection
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|a depthwise atrous convolution
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|a hand-crafted features
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|a two stream residual network
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|a Sinkhorn loss
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|a misalignments
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|a multi-scale
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|a faster region-based convolutional neural network (FRCNN)
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|a Unmanned Aerial Vehicles (UAV)
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|a pixel-wise classification
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| 653 |
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|a deep convolutional networks
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| 653 |
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|a feature engineering
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| 653 |
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|a post-disaster
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| 653 |
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|a convolutional neural networks
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|a global convolution network
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|a unsupervised segmentation
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|a xBD
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|a CycleGAN
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|a ISPRS vaihingen
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|a text image matching
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|a image classification
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|a visibility
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|a infrastructure
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|a anomaly detection
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|a road
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|a despeckling
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| 653 |
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|a conditional random field (CRF)
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| 653 |
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|a Research and information: general / bicssc
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| 653 |
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|a Batch Normalization
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| 653 |
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|a densenet
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| 653 |
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|a framework
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|a high-resolution representations
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|a UAV multispectral images
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|a orthophoto
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|a precision agriculture
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|a lifting scheme
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|a plant disease detection
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| 653 |
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|a result correction
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|a mapping
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|a edge enhancement
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|a monitoring
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|a adversarial learning
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|a scene classification
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|a water identification
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|a Open Street Map
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| 653 |
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|a object-based
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|a road extraction
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| 653 |
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|a deep features
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| 653 |
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|a generative adversarial networks
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|a hyperspectral image classification
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|a urban forests
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|a LSTM
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|a synthetic aperture radar
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|a EfficientNets
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|a outline extraction
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|a satellites
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|a object detection
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|a nearest feature selector
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|a LSTM network
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|a single-shot multibox detector (SSD)
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|a orthophotos registration
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| 653 |
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|a satellite
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|a high spatial resolution remote sensing
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| 653 |
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|a open-set domain adaptation
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| 653 |
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|a water index
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|a pareto ranking
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|a remote sensing
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|a CNN
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|a pavement markings
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|a single-shot
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|a OUDN algorithm
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|a high-resolution remote sensing imagery
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| 653 |
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|a Sentinel-1
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| 653 |
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|a semantic segmentation
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| 653 |
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|a sub-pixel
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| 653 |
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|a Generative Adversarial Networks
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| 653 |
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|a high-resolution remote sensing image
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| 653 |
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|a orthophotos segmentation
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| 653 |
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|a convolutional neural network
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| 653 |
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|a SAR
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| 653 |
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|a triplet networks
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| 700 |
1 |
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|a Pasolli, Edoardo
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| 700 |
1 |
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|a Bazi, Yakoub
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| 700 |
1 |
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|a Pasolli, Edoardo
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
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|a 10.3390/books978-3-0365-0987-7
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| 856 |
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|u https://directory.doabooks.org/handle/20.500.12854/76425
|z DOAB: description of the publication
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/3860
|7 0
|x Verlag
|3 Volltext
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|a 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.
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