05921nam 2201501z- 450 991055774790332120220111(CKB)5400000000045863(oapen)https://directory.doabooks.org/handle/20.500.12854/76425(oapen)doab76425(EXLCZ)99540000000004586320202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierAdvanced Deep Learning Strategies for the Analysis of Remote Sensing ImagesBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (438 p.)3-0365-0986-0 3-0365-0987-9 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.Research and information: generalbicssc3D informationadversarial learninganomaly detectionBatch Normalizationbuilding damage assessmentCNNconditional random field (CRF)convolutionconvolutional neural networkconvolutional neural networksCycleGANdata augmentationdeep convolutional networksdeep featuresdeep learningdensenetDenseUNetdepthwise atrous convolutiondesertdespecklingedge enhancementEfficientNetsfaster region-based convolutional neural network (FRCNN)feature engineeringfeature fusionframeworkgenerative adversarial networksGenerative Adversarial Networksglobal convolution networkhand-crafted featureshigh spatial resolution remote sensinghigh-resolution remote sensing imagehigh-resolution remote sensing imageryhigh-resolution representationshyperspectral image classificationimage classificationinfrastructureISPRS vaihingenLandsat-8lifting schemeLSTMLSTM networkmachine learningmappingmin-max entropymisalignmentsmonitoringmulti-scalenearest feature selectorneural networksobject detectionobject-basedOpen Street Mapopen-set domain adaptationorthophotoorthophotos registrationorthophotos segmentationOUDN algorithmoutline extractionpareto rankingpavement markingspixel-wise classificationplant disease detectionpost-disasterprecision agricultureremote sensingremote sensing imageryresult correctionroadroad extractionSARsatellitesatellitesscene classificationsemantic segmentationSentinel-1single-shotsingle-shot multibox detector (SSD)Sinkhorn losssub-pixelsuper-resolutionsynthetic aperture radartext image matchingtriplet networkstwo stream residual networkU-NetUAV multispectral imagesUnmanned Aerial Vehicles (UAV)unsupervised segmentationurban forestsvisibilitywater identificationwater indexwildfire detectionxBDResearch and information: generalBazi Yakoubedt1327926Pasolli EdoardoedtBazi YakoubothPasolli EdoardoothBOOK9910557747903321Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images3038285UNINA