LEADER 05908nam 2201489z- 450 001 9910557747903321 005 20231214133058.0 035 $a(CKB)5400000000045863 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76425 035 $a(EXLCZ)995400000000045863 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Deep Learning Strategies for the Analysis of Remote Sensing Images 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (438 p.) 311 $a3-0365-0986-0 311 $a3-0365-0987-9 330 $aThe 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. 606 $aResearch & information: general$2bicssc 610 $asynthetic aperture radar 610 $adespeckling 610 $amulti-scale 610 $aLSTM 610 $asub-pixel 610 $ahigh-resolution remote sensing imagery 610 $aroad extraction 610 $amachine learning 610 $aDenseUNet 610 $ascene classification 610 $alifting scheme 610 $aconvolution 610 $aCNN 610 $aimage classification 610 $adeep features 610 $ahand-crafted features 610 $aSinkhorn loss 610 $aremote sensing 610 $atext image matching 610 $atriplet networks 610 $aEfficientNets 610 $aLSTM network 610 $aconvolutional neural network 610 $awater identification 610 $awater index 610 $asemantic segmentation 610 $ahigh-resolution remote sensing image 610 $apixel-wise classification 610 $aresult correction 610 $aconditional random field (CRF) 610 $asatellite 610 $aobject detection 610 $aneural networks 610 $asingle-shot 610 $adeep learning 610 $aglobal convolution network 610 $afeature fusion 610 $adepthwise atrous convolution 610 $ahigh-resolution representations 610 $aISPRS vaihingen 610 $aLandsat-8 610 $afaster region-based convolutional neural network (FRCNN) 610 $asingle-shot multibox detector (SSD) 610 $asuper-resolution 610 $aremote sensing imagery 610 $aedge enhancement 610 $asatellites 610 $aopen-set domain adaptation 610 $aadversarial learning 610 $amin-max entropy 610 $apareto ranking 610 $aSAR 610 $aSentinel?1 610 $aOpen Street Map 610 $aU?Net 610 $adesert 610 $aroad 610 $ainfrastructure 610 $amapping 610 $amonitoring 610 $adeep convolutional networks 610 $aoutline extraction 610 $amisalignments 610 $anearest feature selector 610 $ahyperspectral image classification 610 $atwo stream residual network 610 $aBatch Normalization 610 $aplant disease detection 610 $aprecision agriculture 610 $aUAV multispectral images 610 $aorthophotos registration 610 $a3D information 610 $aorthophotos segmentation 610 $awildfire detection 610 $aconvolutional neural networks 610 $adensenet 610 $agenerative adversarial networks 610 $aCycleGAN 610 $adata augmentation 610 $apavement markings 610 $avisibility 610 $aframework 610 $aurban forests 610 $aOUDN algorithm 610 $aobject-based 610 $ahigh spatial resolution remote sensing 610 $aGenerative Adversarial Networks 610 $apost-disaster 610 $abuilding damage assessment 610 $aanomaly detection 610 $aUnmanned Aerial Vehicles (UAV) 610 $axBD 610 $afeature engineering 610 $aorthophoto 610 $aunsupervised segmentation 615 7$aResearch & information: general 700 $aBazi$b Yakoub$4edt$01327926 702 $aPasolli$b Edoardo$4edt 702 $aBazi$b Yakoub$4oth 702 $aPasolli$b Edoardo$4oth 906 $aBOOK 912 $a9910557747903321 996 $aAdvanced Deep Learning Strategies for the Analysis of Remote Sensing Images$93038285 997 $aUNINA