Graph Neural Network for Hyperspectral Image Clustering

Erscheinungsjahr 2025
ISBN: 978-981-967709-2
Verlag: Springer

Buch, Englisch, 157 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 459 g

Reihe: Intelligent Perception and Information Processing

This 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.

Zielgruppe

Research

Autoren/Hrsg.

Ding, Yao

Zhang, Zhili

Hu, Haojie

Guan, Renxiang

Feng, Jie

Lv, Zhiyong

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Biomedizin, Medizinische Forschung, Klinische Studien
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik

Inhaltsverzeichnis

Introduction.- 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.

Über Autor(innen)

Dr. Yao Ding received the M.S. and Ph.D. degree from the Key Laboratory of Optical Engineering, Xi’an Research Institute of High Technology, Xi’an 710025, China, in 2013 and 2022. His research interests include neural network, computer vision, image processing, and hyperspectral image clustering. He has published several papers in IEEE Trans. on Geoscience and Remote Sensing (TGRS), Information Sciences (INS), Expert Systems with Applications (ESWA), Defence Technology (DT), IEEE Geoscience and Remote Sensing Letters (GRSL), Neurocomputing, etc. Furthermore, he has published three monographs, and six patents have been applied. He has received excellent doctoral dissertations from the China Simulation Society and the China Ordnance Industry Society in 2023. He also has received HIGHLY CITED AWARDS from Defence Technology (DT) journal.

Dr. Zhili Zhang received the B.S., M.S., and Ph.D. degrees in 1988, 1991, and 2001, respectively, all from Rocket Force University of Engineering, China. He is currently a professor in Rocket Force University of Engineering, China. Dr. Zhang has achieved fruitful results in the field he is studying, and several Prizes have been awarded, including:1) One First Class Prize and Two Second Class Prizes of The State Science and Technology Progress Award of China; 2) One Second Class Prize of The State Technology Invention Award of China; 3) Twelve Provincial/Ministerial level awards. In addition, he has published six monographs, four briefs, and over a hundred SCI and EI research papers, including four hot papers and ten highly cited papers of ESI. Furthermore, he has been authorized over fifty invention patents.

Dr. Haojie Hu received his M.S. and Ph.D. degrees from Xi’an Research Institute of High Technology, Xi’an, China, in 2016, and 2019, respectively. He is currently a lecturer in Xi’an Research Institute of High Technology. Dr. Hu focuses on the research of machine learning and hyperspectral image processing. Since 2016, Dr. Hu has published 16 peer-reviewed technical papers in international journals and conferences.

Dr. Renxiang Guan received the B.S. degree from China University of Geosciences (CUG), Wuhan, China in 2023. He is currently pursuing the master’s degree with the National University of Dense Technology (NUDT), Changsha, China. He has published several peer-reviewed papers, including AAAI, TGRS, ICASSP, KBS, IJCNN, and Remote Sensing. His current research interests include deep graph clustering, hyperspectral image processing.

Prof. Jie Feng received the B.S. degree from Chang’an University, Xi’an, China, in 2008, and the Ph.D. degree from Xidian University, Xi’an, in 2014. She is currently a professor with the Key Laboratory of Intelligent Perception and Image Understanding, Xidian University. Her research interests include remote sensing image processing, deep learning, and machine learning.

Dr. Zhiyong Lv received the M.S. and Ph.D. degrees from the School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China, in 2008 and 2014, respectively. He was an Engineer of surveying and worked with The First Institute of Photogrammetry and Remote Sensing, Xi’an, China, from 2008 to 2011. He is currently working with the School of Computer Science and Engineering, Xi’an University of Technology, Xi’an. His research interests include hyperspectral and high-resolution remotely sensed image processing, spatial feature extraction, neural networks, pattern recognition, deep learning, and remote sensing applications.