01303nam a2200349 i 4500991001007809707536101229s2008 it a 101 0 ita d9788888479200b13945798-39ule_instDip.to Matematicaeng510AMS 00B10AMS 00A05Conferenze e Seminari 2007-2008 /volume redatto a cura di F. Ferrara, L. Giacardi, M. MoscaTorino :KWB,c2008391 p. :ill. ;24 cmConferenze e SeminariAt head of title: Associazione Subalpina Mathesis ; Seminario di Storia delle matematiche "Tullio Viola"Conference proceedingsCollections of papersGeneral mathematicsFerrara, F.Giacardi, LiviaMosca, MirandaAssociazione Subalpina MathesisSeminario di Storia delle Matematiche "Tullio Viola".b1394579817-06-1429-12-10991001007809707536LE013 00B MAT19 (2008)12013000213729le013gE30.00-l- 00000.i1521718830-12-10Conferenze e Seminari 2007-2008251515UNISALENTOle01329-12-10ma -itait 0000737nam0-22002531i-450 99000264264040332120240422100309.0FED01000264264(Aleph)000264264FED0100026426420000920d1962----km-y0itay50------baengUSInventories<<A >> guide to their control,costing and effect upon income and taxesby Hof fmann,R.A.New YorkThe Ronald Press Co.1962x, 382 p.23 cmHoffman,Raymond A.108095ITUNINARICAUNIMARCBK990002642640403321C3-P25-21-RAs.i.ECAECAInventories4153929UNINA03313nam 22006495 450 991091049310332120241127115246.09789819780099981978009810.1007/978-981-97-8009-9(CKB)36679868200041(MiAaPQ)EBC31806104(Au-PeEL)EBL31806104(DE-He213)978-981-97-8009-9(EXLCZ)993667986820004120241127d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierGraph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images /by Yao Ding, Zhili Zhang, Haojie Hu, Fang He, Shuli Cheng, Yijun Zhang1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (189 pages)Intelligent Perception and Information Processing,3059-38169789819780082 981978008X Introduction -- Graph sample and aggregate-attention network for hyperspectral image classification -- Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification -- Pixel and hyperpixel level feature combining for hyperspectral image classification -- Global dynamic graph optimization for hyperspectral image classification -- Exploring relationship between transformer and graph convolution for hyperspectral image classification.This book deals with hyperspectral image classification using graph neural network methods, focusing on classification model designing, graph information dissemination, and graph construction. In the book, various graph neural network based classifiers have been proposed for hyperspectral image classification to improve the classification accuracy. This book has promoted the application of graph neural network in hyperspectral image classification, providing reference for remote sensing image processing. It will be a useful reference for researchers in remote sensing image processing and image neural network design.Intelligent Perception and Information Processing,3059-3816Image processingNeural networks (Computer science)Machine learningImage ProcessingMathematical Models of Cognitive Processes and Neural NetworksMachine LearningImage processing.Neural networks (Computer science).Machine learning.Image Processing.Mathematical Models of Cognitive Processes and Neural Networks.Machine Learning.621.382Ding Yao1668378Zhang Zhili1776890Hu Haojie1776891He Fang1705440Cheng Shuli1776892Zhang Yijun1776893MiAaPQMiAaPQMiAaPQBOOK9910910493103321Graph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images4296128UNINA