01628nam0 22003853i 450 LO1031853120251003044208.0IT8410938 19931104d1982 ||||0itac50 baitaitz01i xxxe z01nˆLa ‰scommessa del sindacatoBruno UgoliniRomaEditori riuniti1982102 p.19 cm.Tendenze16001CFI00525062001 Tendenze16SindacatiItaliaFIRCFIC061796E331.87ORGANIZZAZIONE SINDACALE20331.88SINDACATI19331.880945SINDACATI. Italia23Associazioni sindacaliOrganizzazioni sindacaliOrganizzazione sindacaleSindacatiAssociazioni sindacaliSindacatiOrganizzazioni sindacaliSindacatiOrganizzazione sindacaleUgolini, BrunoLO1V042028070552591ITIT-00000019931104IT-BN0095 IT-NA0230 NAP 01POZZO LIB.Vi sono collocati fondi di economia, periodici di ingegneria e scienze, periodici di economia e statistica e altri fondi comprendenti documenti di economia pervenuti in dono. LO10318531Biblioteca Centralizzata di Ateneo1 v. 01POZZO LIB.F. SANTI 407 0101 0060032815E VMA 1 v. (Precedente collocazione S 409)B 2022110920221109 01 IRScommessa del sindacato2961458UNISANNIO09070nam 22025213a 450 991036775560332120250203235431.09783038976851303897685710.3390/books978-3-03897-685-1(CKB)4100000010106161(oapen)https://directory.doabooks.org/handle/20.500.12854/51488(ScCtBLL)79d70424-93d2-4093-ab8b-adfdad8a624a(OCoLC)1163826722(oapen)doab51488(oapen)51488(EXLCZ)99410000001010616120250203i20192019 uu engurmn|---annantxtrdacontentcrdamediacrrdacarrierLearning to Understand Remote Sensing Images: Volume 1Qi WangMDPI - Multidisciplinary Digital Publishing Institute2019Basel, Switzerland :MDPI,2019.1 electronic resource (414 pages)9783038976844 3038976849 With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.Computer sciencebicsscmetadataimage classificationsensitivity analysisROI detectionresidual learningimage alignmentadaptive convolutional kernelsHough transformclass imbalanceland surface temperatureinundation mappingmultiscale representationobject-basedconvolutional neural networksscene classificationmorphological profileshyperedge weight estimationhyperparameter sparse representationsemantic segmentationvehicle classificationfloodLandsat imagerytarget detectionmulti-sensorbuilding damage detectionoptimized kernel minimum noise fraction (OKMNF)sea-land segmentationnonlinear classificationland useSAR imageryanti-noise transfer networksub-pixel change detectionRadon transformsegmentationremote sensing image retrievalTensorFlowconvolutional neural networkparticle swarm optimizationoptical sensorsmachine learningmixed pixeloptical remotely sensed imagesobject-based image analysisvery high resolution imagessingle stream optimizationship detectionice concentrationonline learningmanifold rankingdictionary learningurban surface water extractionsaliency detectionspatial attraction model (SAM)quality assessmentFuzzy-GA decision making systemland cover changemulti-view canonical correlation analysis ensembleland coversemantic labelingsparse representationdimensionality expansionspeckle filtershyperspectral imageryfully convolutional networkinfrared imageSiamese neural networkRandom Forests (RF)feature matchingcolor matchinggeostationary satellite remote sensing imagechange feature analysisroad detectiondeep learningaerial imagesimage segmentationaerial imagemulti-sensor image matchingHJ-1A/B CCDendmember extractionhigh resolutionmulti-scale clusteringheterogeneous domain adaptationhard classificationregional land coverhypergraph learningautomatic cluster number determinationdilated convolutionMSERsemi-supervised learninggateSynthetic Aperture Radar (SAR)downscalingconditional random fieldsurban heat islandhyperspectral imageremote sensing image correctionskip connectionISPRSspatial distributiongeo-referencingSupport Vector Machine (SVM)very high resolution (VHR) satellite imageclassificationensemble learningsynthetic aperture radarconservationconvolutional neural network (CNN)THEOSvisible light and infrared integrated cameravehicle localizationstructured sparsitytexture analysisDSFATNCNNimage registrationUAVunsupervised classificationSVMsSAR imagefuzzy neural networkdimensionality reductionGeoEye-1feature extractionsub-pixelenergy distribution optimizingsaliency analysisdeep convolutional neural networkssparse and low-rank graphhyperspectral remote sensingtensor low-rank approximationoptimal transportSELFspatiotemporal context learningModest AdaBoosttopic modellingmulti-seasonalSegment-Tree Filteringlocality informationGF-4 PMSimage fusionwavelet transformhashingmachine learning techniquessatellite imagesclimate changeroad segmentationremote sensingtensor sparse decompositionConvolutional Neural Network (CNN)multi-task learningdeep salient featurespecklecanonical correlation weighted votingfully convolutional network (FCN)despecklingmultispectral imageryratio imageslinear spectral unmixinghyperspectral image classificationmultispectral imageshigh resolution imagemulti-objectiveconvolution neural networktransfer learning1-dimensional (1-D)threshold stabilityLandsatkernel methodphase congruencysubpixel mapping (SPM)tensorMODISGSHHG databasecompressive sensingComputer scienceWang Qi646598ScCtBLLScCtBLLBOOK9910367755603321Learning to Understand Remote Sensing Images3024026UNINA