LEADER 03747nam 22006975 450 001 9911020416403321 005 20250809130211.0 010 $a981-9677-10-6 024 7 $a10.1007/978-981-96-7710-8 035 $a(MiAaPQ)EBC32257389 035 $a(Au-PeEL)EBL32257389 035 $a(CKB)40158919400041 035 $a(DE-He213)978-981-96-7710-8 035 $a(EXLCZ)9940158919400041 100 $a20250809d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGraph Neural Network for Hyperspectral Image Clustering /$fby Yao Ding, Zhili Zhang, Haojie Hu, Renxiang Guan, Jie Feng, Zhiyong Lv 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (259 pages) 225 1 $aIntelligent Perception and Information Processing,$x3059-3816 311 08$a981-9677-09-2 327 $aIntroduction -- Self-supervised Efficient Low-pass Contrastive Graph Clustering for Hyperspectral Images -- Self-Supervised Locality Preserving Low-Pass Graph Convolutional Embedding for Large-Scale Hyperspectral Image Clustering -- Adaptive Homophily Clustering: A Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image -- Pixel-superpixel Contrastive Learning And Pseudo-label correction For Hyperspectral Image Clustering -- Contrastive Multiview Subspace Clustering of Hyperspectral Images Based on Graph Convolutional Networks. 330 $aThis book investigates detailed hyperspectral image clustering using graph neural network (graph learning) methods, focusing on the overall construction of the model, design of self-supervised methods, image pre-processing, and feature extraction of graph information. Multiple graph neural network-based clustering methods for hyperspectral images are proposed, effectively improving the clustering accuracy of hyperspectral images and taking an important step towards the practical application of hyperspectral images. This book is innovative in content and emphasizes the integration of theory with practice, which can be used as a reference book for graduate students, senior undergraduate students, researchers, and engineering technicians in related majors such as electronic information engineering, computer application technology, automation, instrument science and technology, remote sensing. . 410 0$aIntelligent Perception and Information Processing,$x3059-3816 606 $aImage processing 606 $aMedicine$xResearch 606 $aBiology$xResearch 606 $aNeural networks (Computer science) 606 $aMachine learning 606 $aImage Processing 606 $aBiomedical Research 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aMachine Learning 615 0$aImage processing. 615 0$aMedicine$xResearch. 615 0$aBiology$xResearch. 615 0$aNeural networks (Computer science) 615 0$aMachine learning. 615 14$aImage Processing. 615 24$aBiomedical Research. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aMachine Learning. 676 $a621.382 700 $aDing$b Yao$01668378 701 $aZhang$b Zhili$01776890 701 $aHu$b Haojie$01776891 701 $aGuan$b Renxiang$01840343 701 $aFeng$b Jie$01733470 701 $aLv$b Zhiyong$01840344 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020416403321 996 $aGraph Neural Network for Hyperspectral Image Clustering$94419881 997 $aUNINA