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dc.contributor.editorBazi, Yakoub
dc.contributor.editorPasolli, Edoardo
dc.date.accessioned2022-01-11T13:31:33Z
dc.date.available2022-01-11T13:31:33Z
dc.date.issued2021
dc.identifierONIX_20220111_9783036509860_161
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/76425
dc.description.abstractThe 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.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::G Reference, information & interdisciplinary subjects::GP Research & information: general
dc.subject.othersynthetic aperture radar
dc.subject.otherdespeckling
dc.subject.othermulti-scale
dc.subject.otherLSTM
dc.subject.othersub-pixel
dc.subject.otherhigh-resolution remote sensing imagery
dc.subject.otherroad extraction
dc.subject.othermachine learning
dc.subject.otherDenseUNet
dc.subject.otherscene classification
dc.subject.otherlifting scheme
dc.subject.otherconvolution
dc.subject.otherCNN
dc.subject.otherimage classification
dc.subject.otherdeep features
dc.subject.otherhand-crafted features
dc.subject.otherSinkhorn loss
dc.subject.otherremote sensing
dc.subject.othertext image matching
dc.subject.othertriplet networks
dc.subject.otherEfficientNets
dc.subject.otherLSTM network
dc.subject.otherconvolutional neural network
dc.subject.otherwater identification
dc.subject.otherwater index
dc.subject.othersemantic segmentation
dc.subject.otherhigh-resolution remote sensing image
dc.subject.otherpixel-wise classification
dc.subject.otherresult correction
dc.subject.otherconditional random field (CRF)
dc.subject.othersatellite
dc.subject.otherobject detection
dc.subject.otherneural networks
dc.subject.othersingle-shot
dc.subject.otherdeep learning
dc.subject.otherglobal convolution network
dc.subject.otherfeature fusion
dc.subject.otherdepthwise atrous convolution
dc.subject.otherhigh-resolution representations
dc.subject.otherISPRS vaihingen
dc.subject.otherLandsat-8
dc.subject.otherfaster region-based convolutional neural network (FRCNN)
dc.subject.othersingle-shot multibox detector (SSD)
dc.subject.othersuper-resolution
dc.subject.otherremote sensing imagery
dc.subject.otheredge enhancement
dc.subject.othersatellites
dc.subject.otheropen-set domain adaptation
dc.subject.otheradversarial learning
dc.subject.othermin-max entropy
dc.subject.otherpareto ranking
dc.subject.otherSAR
dc.subject.otherSentinel–1
dc.subject.otherOpen Street Map
dc.subject.otherU–Net
dc.subject.otherdesert
dc.subject.otherroad
dc.subject.otherinfrastructure
dc.subject.othermapping
dc.subject.othermonitoring
dc.subject.otherdeep convolutional networks
dc.subject.otheroutline extraction
dc.subject.othermisalignments
dc.subject.othernearest feature selector
dc.subject.otherhyperspectral image classification
dc.subject.othertwo stream residual network
dc.subject.otherBatch Normalization
dc.subject.otherplant disease detection
dc.subject.otherprecision agriculture
dc.subject.otherUAV multispectral images
dc.subject.otherorthophotos registration
dc.subject.other3D information
dc.subject.otherorthophotos segmentation
dc.subject.otherwildfire detection
dc.subject.otherconvolutional neural networks
dc.subject.otherdensenet
dc.subject.othergenerative adversarial networks
dc.subject.otherCycleGAN
dc.subject.otherdata augmentation
dc.subject.otherpavement markings
dc.subject.othervisibility
dc.subject.otherframework
dc.subject.otherurban forests
dc.subject.otherOUDN algorithm
dc.subject.otherobject-based
dc.subject.otherhigh spatial resolution remote sensing
dc.subject.otherGenerative Adversarial Networks
dc.subject.otherpost-disaster
dc.subject.otherbuilding damage assessment
dc.subject.otheranomaly detection
dc.subject.otherUnmanned Aerial Vehicles (UAV)
dc.subject.otherxBD
dc.subject.otherfeature engineering
dc.subject.otherorthophoto
dc.subject.otherunsupervised segmentation
dc.titleAdvanced Deep Learning Strategies for the Analysis of Remote Sensing Images
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-0987-7
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036509860
oapen.relation.isbn9783036509877
oapen.pages438
oapen.place.publicationBasel, Switzerland


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