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.

Studies main problems of graph neural network classifiers for hyperspectral image classification Provides a design method for hyperspectral image classification based on graph neural network Paves the way for future multimodal data fusion and 3D remote sensing image processing

Autorentext

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, China, in 2013 and 2022, respectively. 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 Defense Technology (DT) journal. At present, he has Ten highly cited papers of ESI. In addition, he is also the reviewer of TGRS, TNNLS, PR, JAG, KBS, etc. He has also served as a Guest Editor of the Forecasting.

Dr. Zhili Zhang received the B.S., M.S., and Ph.D. degrees in 1988, 1991, and 2001, respectively, all from Xi'an Research Institute of High Technology, Xi'an, China. He is currently a professor at Xi'an Research Institute of High Technology, China. His research interests include inertial navigation systems theory, system simulation, image processing and Position and navigation.

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 at 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. Fang He received the B.S., M.S. and Ph.D. degrees from the Xi'an Research Institute of High Technology, Xi'an, China, in 2014, 2016, and 2021, respectively. Now, she is a lecturer at the Xi'an Research Institute of High Technology.

Dr. He focuses on the research of machine learning and image processing. Her recent research interests include: hyperspectral image classification and anomaly detection, big data processing. Dr. He has published 19 technical papers in international journals and conferences. Among them, 2 papers were published on JCR Q1 journals with IF 10.4 and 8.2 respectively, and 1 paper is published on CCF A journal with IF 8.9. Dr. He also got school excellent doctoral dissertation award in 2022.

Dr. Shuli Cheng received his B.E. and Ph.D. degrees in the School of Computer Science and Technology from Xinjiang University, Urumqi, China, in 2016 and 2021, respectively. He was a Post-Doctoral Researcher with the Mathematics and System Science, Xinjiang University, Urumqi, China, from 2021 to 2023.Since Nov. 2022, Dr. Cheng has been in the School of Computer Science and Technology, Xinjiang University as an Associate Professor.

He has published more than 30 papers in leading and key journals and conferences, such as IEEE TCSVT, IEEE TGRS, ESWA, ASOC, KBS, and PR. He serves as an PC Member or a reviewer for various international conferences and journals, such as AAAI, ICCV, IEEE TMM, and IEEE TCSVT.

Dr. Yijun Zhang received his B.E. degree in Simulation Engineering and M. E. degree in Operations Research from the People's Liberation Army University of Science and Technology in 2006 and 2010 respectively, and the Ph.D. degree in Computational Science and Technology from the Xi'an Research Institute of High Technology in 2023. Since July 2023, Dr. Zhang has been working as an Associate Professor at the Experimental Training Base of the National University of Defense Technology, in Xi'an, China.

Inhalt

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.