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100 1 _aDing, Yao.
_eauthor.
_0(orcid)0000-0003-2040-2640
_1https://orcid.org/0000-0003-2040-2640
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGraph Neural Network for Feature Extraction and Classification of Hyperspectral Remote Sensing Images
_h[electronic resource] /
_cby Yao Ding, Zhili Zhang, Haojie Hu, Fang He, Shuli Cheng, Yijun Zhang.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXII, 183 p. 73 illus., 67 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Perception and Information Processing,
_x3059-3816
505 0 _aIntroduction -- 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.
520 _aThis 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.
541 _fUABC ;
_cPerpetuidad
650 0 _aImage processing.
650 0 _aNeural networks (Computer science) .
650 0 _aMachine learning.
650 1 4 _aImage Processing.
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
650 2 4 _aMachine Learning.
700 1 _aZhang, Zhili.
_eauthor.
_0(orcid)0000-0003-4894-5495
_1https://orcid.org/0000-0003-4894-5495
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aHu, Haojie.
_eauthor.
_0(orcid)0000-0002-6645-8853
_1https://orcid.org/0000-0002-6645-8853
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aHe, Fang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aCheng, Shuli.
_eauthor.
_0(orcid)0000-0003-4759-0282
_1https://orcid.org/0000-0003-4759-0282
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZhang, Yijun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819780082
776 0 8 _iPrinted edition:
_z9789819780105
776 0 8 _iPrinted edition:
_z9789819780112
830 0 _aIntelligent Perception and Information Processing,
_x3059-3816
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-97-8009-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c276886
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